session-1/lecture-1.ipynb

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    "# Session 1: Introduction to Tensorflow\n",
    "<p class='lead'>\n",
    "Creative Applications of Deep Learning with Tensorflow<br />\n",
    "Parag K. Mital<br />\n",
    "Kadenze, Inc.<br />\n",
    "</p>\n",
    "\n",
    "<a name=\"learning-goals\"></a>\n",
    "# Learning Goals\n",
    "\n",
    "* Learn the basic idea behind machine learning: learning from data and discovering representations\n",
    "* Learn how to preprocess a dataset using its mean and standard deviation\n",
    "* Learn the basic components of a Tensorflow Graph\n",
    "\n",
    "# Table of Contents\n",
    "<!-- MarkdownTOC autolink=true autoanchor=true bracket=round -->\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "    - [Promo](#promo)\n",
    "    - [Session Overview](#session-overview)\n",
    "- [Learning From Data](#learning-from-data)\n",
    "    - [Deep Learning vs. Machine Learning](#deep-learning-vs-machine-learning)\n",
    "    - [Invariances](#invariances)\n",
    "    - [Scope of Learning](#scope-of-learning)\n",
    "    - [Existing datasets](#existing-datasets)\n",
    "- [Preprocessing Data](#preprocessing-data)\n",
    "    - [Understanding Image Shapes](#understanding-image-shapes)\n",
    "    - [The Batch Dimension](#the-batch-dimension)\n",
    "    - [Mean/Deviation of Images](#meandeviation-of-images)\n",
    "    - [Dataset Preprocessing](#dataset-preprocessing)\n",
    "    - [Histograms](#histograms)\n",
    "    - [Histogram Equalization](#histogram-equalization)\n",
    "- [Tensorflow Basics](#tensorflow-basics)\n",
    "    - [Variables](#variables)\n",
    "    - [Tensors](#tensors)\n",
    "    - [Graphs](#graphs)\n",
    "    - [Operations](#operations)\n",
    "    - [Tensor](#tensor)\n",
    "    - [Sessions](#sessions)\n",
    "    - [Tensor Shapes](#tensor-shapes)\n",
    "    - [Many Operations](#many-operations)\n",
    "- [Convolution](#convolution)\n",
    "    - [Creating a 2-D Gaussian Kernel](#creating-a-2-d-gaussian-kernel)\n",
    "    - [Convolving an Image with a Gaussian](#convolving-an-image-with-a-gaussian)\n",
    "    - [Convolve/Filter an image using a Gaussian Kernel](#convolvefilter-an-image-using-a-gaussian-kernel)\n",
    "    - [Modulating the Gaussian with a Sine Wave to create Gabor Kernel](#modulating-the-gaussian-with-a-sine-wave-to-create-gabor-kernel)\n",
    "    - [Manipulating an image with this Gabor](#manipulating-an-image-with-this-gabor)\n",
    "- [Homework](#homework)\n",
    "- [Next Session](#next-session)\n",
    "- [Reading Material](#reading-material)\n",
    "\n",
    "<!-- /MarkdownTOC -->\n",
    "\n",
    "<a name=\"introduction\"></a>\n",
    "# Introduction\n",
    "\n",
    "This course introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms.  We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks.  A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications.  We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image.  Deep learning offers enormous potential for creative applications and in this course we interrogate what's possible.  Through practical applications and guided homework assignments, you'll be expected to create datasets, develop and train neural networks, explore your own media collections using existing state-of-the-art deep nets, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.​​\n",
    "\n",
    "<a name=\"promo\"></a>\n",
    "## Promo\n",
    "\n",
    "Deep learning has emerged at the forefront of nearly every major computational breakthrough in the last 4 years.  It is no wonder that it is already in many of the products we use today, from netflix or amazon's personalized recommendations; to the filters that block our spam; to ways that we interact with personal assistants like Apple's Siri or Microsoft Cortana, even to the very ways our personal health is monitored.  And sure deep learning algorithms are capable of some amazing things.  But it's not just science applications that are benefiting from this research.\n",
    "\n",
    "Artists too are starting to explore how Deep Learning can be used in their own practice.  Photographers are starting to explore different ways of exploring visual media.  Generative artists are writing algorithms to create entirely new aesthetics. Filmmakers are exploring virtual worlds ripe with potential for procedural content.\n",
    "\n",
    "In this course, we're going straight to the state of the art.  And we're going to learn it all.  We'll see how to make an algorithm paint an image, or hallucinate objects in a photograph.  We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets to using them to self organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of other images.  We'll even see how to teach a computer to read and synthesize new phrases.\n",
    "\n",
    "But we won't just be using other peoples code to do all of this.  We're going to develop everything ourselves using Tensorflow and I'm going to show you how to do it.  This course isn't just for artists nor is it just for programmers.  It's for people that want to learn more about how to apply deep learning with a hands on approach, straight into the python console, and learn what it all means through creative thinking and interaction.\n",
    "\n",
    "I'm Parag Mital, artist, researcher and Director of Machine Intelligence at Kadenze.  For the last 10 years, I've been exploring creative uses of computational models making use of machine and deep learning, film datasets, eye-tracking, EEG, and fMRI recordings exploring applications such as generative film experiences, augmented reality hallucinations, and expressive control of large audiovisual corpora.\n",
    "\n",
    "But this course isn't just about me.  It's about bringing all of you together.  It's about bringing together different backgrounds, different practices, and sticking all of you in the same virtual room, giving you access to state of the art methods in deep learning, some really amazing stuff, and then letting you go wild on the Kadenze platform.  We've been working very hard to build a platform for learning that rivals anything else out there for learning this stuff.\n",
    "\n",
    "You'll be able to share your content, upload videos, comment and exchange code and ideas, all led by the course I've developed for us.  But before we get there we're going to have to cover a lot of groundwork.  The basics that we'll use to develop state of the art algorithms in deep learning.  And that's really so we can better interrogate what's possible, ask the bigger questions, and be able to explore just where all this is heading in more depth.  With all of that in mind, Let's get started>\n",
    "\n",
    "Join me as we learn all about Creative Applications of Deep Learning with Tensorflow.\n",
    "\n",
    "<a name=\"session-overview\"></a>\n",
    "## Session Overview\n",
    "\n",
    "We're first going to talk about Deep Learning, what it is, and how it relates to other branches of learning.  We'll then talk about the major components of Deep Learning, the importance of datasets, and the nature of representation, which is at the heart of deep learning.\n",
    "\n",
    "If you've never used Python before, we'll be jumping straight into using libraries like numpy, matplotlib, and scipy. Before starting this session, please check the resources section for a notebook introducing some fundamentals of python programming.  When you feel comfortable with loading images from a directory, resizing, cropping, how to change an image datatype from unsigned int to float32, and what the range of each data type should be, then come back here and pick up where you left off.  We'll then get our hands dirty with Tensorflow, Google's library for machine intelligence.  We'll learn the basic components of creating a computational graph with Tensorflow, including how to convolve an image to detect interesting features at different scales.  This groundwork will finally lead us towards automatically learning our handcrafted features/algorithms.\n",
    "\n",
    "<a name=\"learning-from-data\"></a>\n",
    "# Learning From Data\n",
    "\n",
    "<a name=\"deep-learning-vs-machine-learning\"></a>\n",
    "## Deep Learning vs. Machine Learning\n",
    "\n",
    "So what is this word I keep using, Deep Learning.  And how is it different to Machine Learning?  Well Deep Learning is a *type* of Machine Learning algorithm that uses Neural Networks to learn.  The type of learning is \"Deep\" because it is composed of many layers of Neural Networks.  In this course we're really going to focus on supervised and unsupervised Deep Learning.  But there are many other incredibly valuable branches of Machine Learning such as Reinforcement Learning, Dictionary Learning, Probabilistic Graphical Models and Bayesian Methods (Bishop), or Genetic and Evolutionary Algorithms.  And any of these branches could certainly even be combined with each other or with Deep Networks as well.  We won't really be able to get into these other branches of learning in this course.  Instead, we'll focus more on building \"networks\", short for neural networks, and how they can do some really amazing things.  Before we can get into all that, we're going to need to understand a bit more about data and its importance in deep learning.\n",
    "\n",
    "<a name=\"invariances\"></a>\n",
    "## Invariances\n",
    "\n",
    "Deep Learning requires data.  A lot of it.  It's really one of the major reasons as to why Deep Learning has been so successful.  Having many examples of the thing we are trying to learn is the first thing you'll need before even thinking about Deep Learning.  Often, it is the biggest blocker to learning about something in the world.  Even as a child, we need a lot of experience with something before we begin to understand it.  I find I spend most of my time just finding the right data for a network to learn.  Getting it from various sources, making sure it all looks right and is labeled.  That is a lot of work.  The rest of it is easy as we'll see by the end of this course.\n",
    "\n",
    "Let's say we would like build a network that is capable of looking at an image and saying what object is in the image.  There are so many possible ways that an object could be manifested in an image.  It's rare to ever see just a single object in isolation. In order to teach a computer about an object, we would have to be able to give it an image of an object in every possible way that it could exist.\n",
    "\n",
    "We generally call these ways of existing \"invariances\".  That just means we are trying not to vary based on some factor.  We are invariant to it.  For instance, an object could appear to one side of an image, or another.  We call that translation invariance.  Or it could be from one angle or another.  That's called rotation invariance.  Or it could be closer to the camera, or farther.  and That would be scale invariance.  There are plenty of other types of invariances, such as perspective or brightness or exposure to give a few more examples for photographic images.\n",
    "\n",
    "<a name=\"scope-of-learning\"></a>\n",
    "## Scope of Learning\n",
    "\n",
    "With Deep Learning, you will always need a dataset that will teach the algorithm about the world.  But you aren't really teaching it everything.  You are only teaching it what is in your dataset!  That is a very important distinction.  If I show my algorithm only faces of people which are always placed in the center of an image, it will not be able to understand anything about faces that are not in the center of the image!  Well at least that's mostly true.\n",
    "\n",
    "That's not to say that a network is incapable of transfering what it has learned to learn new concepts more easily.  Or to learn things that might be necessary for it to learn other representations.  For instance, a network that has been trained to learn about birds, probably knows a good bit about trees, branches, and other bird-like hangouts, depending on the dataset.  But, in general, we are limited to learning what our dataset has access to.\n",
    "\n",
    "So if you're thinking about creating a dataset, you're going to have to think about what it is that you want to teach your network.  What sort of images will it see?  What representations do you think your network could learn given the data you've shown it?\n",
    "\n",
    "One of the major contributions to the success of Deep Learning algorithms is the amount of data out there.  Datasets have grown from orders of hundreds to thousands to many millions.   The more data you have, the more capable your network will be at determining whatever its objective is.\n",
    "\n",
    "<a name=\"existing-datasets\"></a>\n",
    "## Existing datasets\n",
    "\n",
    "With that in mind, let's try to find a dataset that we can work with.  There are a ton of datasets out there that current machine learning researchers use.  For instance if I do a quick Google search for Deep Learning Datasets, i can see for instance a link on deeplearning.net, listing a few interesting ones e.g. http://deeplearning.net/datasets/, including MNIST, CalTech, CelebNet, LFW, CIFAR, MS Coco, Illustration2Vec, and there are ton more.  And these are primarily image based.  But if you are interested in finding more, just do a quick search or drop a quick message on the forums if you're looking for something in particular.\n",
    "\n",
    "* MNIST\n",
    "* CalTech\n",
    "* CelebNet\n",
    "* ImageNet: http://www.image-net.org/\n",
    "* LFW\n",
    "* CIFAR10\n",
    "* CIFAR100\n",
    "* MS Coco: http://mscoco.org/home/\n",
    "* WLFDB: http://wlfdb.stevenhoi.com/\n",
    "* Flickr 8k: http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html\n",
    "* Flickr 30k\n",
    "\n",
    "<a name=\"preprocessing-data\"></a>\n",
    "# Preprocessing Data\n",
    "\n",
    "In this section, we're going to learn a bit about working with an image based dataset.  We'll see how image dimensions are formatted as a single image and how they're represented as a collection using a 4-d array.  We'll then look at how we can perform dataset normalization.  If you're comfortable with all of this, please feel free to skip to the next video.\n",
    "\n",
    "We're first going to load some libraries that we'll be making use of."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll be using a popular image dataset for faces called the CelebFaces dataset.  I've provided some helper functions which you can find on the resources page, which will just help us with manipulating images and loading this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://s3.amazonaws.com/cadl/celeb-align/000100.jpg\r"
     ]
    }
   ],
   "source": [
    "from libs import utils\n",
    "# utils.<tab>\n",
    "files = utils.get_celeb_files()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the 50th image in this list of files, and then read the file at that location as an image, setting the result to a variable, `img`, and inspect a bit further what's going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[178 185 191]\n",
      "  [182 189 195]\n",
      "  [149 156 162]\n",
      "  ..., \n",
      "  [216 220 231]\n",
      "  [220 224 233]\n",
      "  [220 224 233]]\n",
      "\n",
      " [[177 184 190]\n",
      "  [182 189 195]\n",
      "  [153 160 166]\n",
      "  ..., \n",
      "  [215 219 230]\n",
      "  [220 224 233]\n",
      "  [220 224 233]]\n",
      "\n",
      " [[177 184 190]\n",
      "  [182 189 195]\n",
      "  [161 168 174]\n",
      "  ..., \n",
      "  [214 218 229]\n",
      "  [219 223 232]\n",
      "  [219 223 232]]\n",
      "\n",
      " ..., \n",
      " [[ 11  14  23]\n",
      "  [ 11  14  23]\n",
      "  [ 11  14  23]\n",
      "  ..., \n",
      "  [  4   7  16]\n",
      "  [  5   8  17]\n",
      "  [  5   8  17]]\n",
      "\n",
      " [[  9  12  21]\n",
      "  [  9  12  21]\n",
      "  [  9  12  21]\n",
      "  ..., \n",
      "  [  8  11  18]\n",
      "  [  5   8  17]\n",
      "  [  5   8  17]]\n",
      "\n",
      " [[  9  12  21]\n",
      "  [  9  12  21]\n",
      "  [  9  12  21]\n",
      "  ..., \n",
      "  [  8  11  18]\n",
      "  [  5   8  17]\n",
      "  [  5   8  17]]]\n"
     ]
    }
   ],
   "source": [
    "img = plt.imread(files[50])\n",
    "# img.<tab>\n",
    "print(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When I print out this image, I can see all the numbers that represent this image.  We can use the function `imshow` to see this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11262a5f8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oGwZZjifQWAtS4AEffC+ryCECqAAKgQzRchSAw9OI8yXceRJweUqXz7188QK3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\nhiYhY74MGZqE/wPLMhA8tcT7RgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11145b470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If nothing is drawn and you are using notebook, try uncommenting the next line:\n",
    "#%matplotlib inline\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"understanding-image-shapes\"></a>\n",
    "## Understanding Image Shapes\n",
    "\n",
    "Let's break this data down a bit more.  We can see the dimensions of the data using the `shape` accessor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 178, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape\n",
    "# (218, 178, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that the image has 218 rows, 178 columns, and 3 color channels corresponding to the Red, Green, and Blue channels of the image, or RGB.  Let's try looking at just one of the color channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x112f62278>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RaH08HqPT6XhMfsAbJ1TonyYck6N1zx77mkqlkM1mEY/Hzf3Q+VL0m/v+EokE+v2+mX+8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dceb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img[:, :, 0], cmap='gray')\n",
    "#plt.imshow(img[:, :, 1], cmap='gray')\n",
    "#plt.imshow(img[:, :, 2], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the special colon operator to say take every value in this dimension.  This is saying, give me every row, every column, and the 0th dimension of the color channels.  What we're seeing is the amount of Red, Green, or Blue contributing to the overall color image.\n",
    "\n",
    "Let's use another helper function which will load every image file in the celeb dataset rather than just give us the filenames like before.  By default, this will just return the first 100 images because loading the entire dataset is a bit cumbersome.  In one of the later sessions, I'll show you how tensorflow can handle loading images using a pipeline so we can load this same dataset.  For now, let's stick with this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgs = utils.get_celeb_imgs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a list containing our images.  Each index of the `imgs` list is another image which we can access using the square brackets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1130e1f98>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rdhNMt5JPjNv5E8nEQt1QbpNMcWLPLjj2kgqHx9/3C9dhaYDYwyduMIS7c6qJc3Cd6yFu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\n/z/z7Ff5eEDEfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112cca710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imgs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"the-batch-dimension\"></a>\n",
    "## The Batch Dimension\n",
    "\n",
    "Remember that an image has a shape describing the height, width, channels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 178, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out we'll often use another convention for storing many images in an array using a new dimension called the batch dimension.  The resulting image shape will be exactly the same, except we'll stick on a new dimension on the beginning... giving us number of images x the height x the width x the number of color channels.\n",
    "\n",
    "N x H x W x C\n",
    "\n",
    "A Color image should have 3 color channels, RGB.\n",
    "\n",
    "We can combine all of our images to have these 4 dimensions by telling numpy to give us an array of all the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 218, 178, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array(imgs)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will only work if every image in our list is exactly the same size.  So if you have a wide image, short image, long image, forget about it.  You'll need them all to be the same size.  If you are unsure of how to get all of your images into the same size, then please please refer to the online resources for the notebook I've provided which shows you exactly how to take a bunch of images of different sizes, and crop and resize them the best we can to make them all the same size.\n",
    "\n",
    "<a name=\"meandeviation-of-images\"></a>\n",
    "## Mean/Deviation of Images\n",
    "\n",
    "Now that we have our data in a single numpy variable, we can do alot of cool stuff.  Let's look at the mean of the batch channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x113ce2be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K9csXl5gD5nuhRrI2erxZ70dETIHWbzg01bof0jIEAH9rErVMzCsiEVZGGM3v\nSQhRT0xW/OPatEWfgHLJLeWsmA9VQrkkiRwhDrHSvS8T8UplFBC2Hnvz7HMylKbF7YysyampAiYo\nqiCTug1WbIfUdoptXQ06kvngWmMrThUkARD2jTEzHG2+6i0d23ChkfVw+aGQoPfdxqLWmb/rtLYG\n9rDagKA2HtgcAnbppS6HYyWyH67lOINjjGCazftKwdda2kjhm3XbW2U+nlkgOd85odGhvelbupS5\ncKdbC+rgHFKgYdjyRIW0oRDcTkdmbWsxW8fPOVXOi/mQMLEG9wLHUqQSj73BJMnsO5mYAoCldvff\nN1MFuSs8ZixtCh2+hhfwzeqbtEq819BUYwys19fBeLGUELDTn9Esoj80nV+3Fa9nRK9sI6NoHLZC\nM1cJkPbedB+jQjiehalN/1UNXIyt6J7GfXVJpg1nHGlo0Dgug0sSIhLMKn6EWGiPpaOp+rJFC0RD\n3CY9o5VUNU6HqnanlF4YrFXHpsU8Qdp96uNF7c66+f2pQm0bY8p/XFuHDwHMuOAjesT9x+WsmC86\nEoNVd3b7IKNuIQL22PTUQmkuDOfgzw4WMoJPCOYKsr66zkSmxo6xPCJlbFjXtWirzDMSky0CdaeK\nacwk7jFGsQeLI8Q1LbVrjpO1KV1C7jCfMG3hK0rv2ldnZPUxC2eFKtZ1RZkG00zLAmwGOccYWJYk\nJ9UBFUFvADfW2v02NmNDNES0xzjbPPTYngSYDG0jFSuRSGgk54joc9H+PB8D8U0KnX2RI4NNA45S\nth9fkzRHp4sq23o7A54V81EON2mItS9Qu50yAPluhzVkdwmJSsvu81KTQa/j6djvYDBoaJM8ig1H\n7aK+OD62LTyN8X1x+1tomjWQ64FHQdXhJc1nDVVfSyubbIEYpxAIEM+jMnJ8aOL5UDZygzPzZOcE\nDC8ogmPQUqqPbfNDYlzLbRugaqkN6/ATHmuPxXCoje+IZqnX03y/tGRKiKHYAFvKIKHXJL0hoOG7\nL7yjZMpJgNe+trA3aR8y4RK8TSjClgvw1H7hQVYFz4Okr6LpjEJOlfNiPuXWmRlu2U8VXs5SaG+v\nIAaJ0fEJK+b8JuXeYf8kzEh1EFrN7b7NQ63qEsDwtbyx+ZoetdO2hWeT/ahMHcyrNXA6n2uZw9Zo\n64ZqhxXpUj+H9uaGWBuLcJCoxNmh0zrikTEVutHbk/MQOMTX2lprGNsGLmq3JpNHkdnMBJlAKRCA\n57MRX1iF6DfrAAAgAElEQVQPzVTuD00sEjlpIDNjReB7NN5txjJG2TEFJBlONBESB0cVngmc45uC\ngkJYVW1sMXL9U9Mnelv5npjvwx/+MJ588kmIWJ6U3/md38F3vvMdfOYzn8E3v/lNvPWtb8VHP/pR\nPPnkk6+qPhJMXU/Z6zu/xH5zERQD4pXUTCN2/WkJVPMtAjQDiscTJeCZ8HEbmRhpzIw5ti3sPTJe\n1XxBxlUTIomf2g6EztQYuz7UetL2NNeG7K4VQlVJzW8OlqwrNRPSHoJ7BwlzNZcDqB0JSGINDL58\n4Hw8ZZArxKpjMB+kwzoy9OBWRZd/PHQU3r/0tu7hIVW7SI5pNGRHRISI8ZlMRyIowkwiLXy5mU+N\ncbax8DBhDspry3wigt/6rd/C008/Hd89//zzePvb344PfOADeP755/H5z38ev/7rv/7q6oMTQ9kC\nXO0WLf/UzUY6TSCmazW0qX3fWhr+o9hfXCtKNzuZYd6eE06X6RqLfdyc+Sqj1vjP0Brh0s821rQS\nlJzhIm8tlxWA6X0QdBxP668Br0ud/kgZIzebFsYhPA2mDlt5nifVEvxQoHcNjIh50AGgB7wL4TCK\nsHNxaf5C291uzpuElolGUgMLOHcwoxAa7WW0kMY+RcwMWxmuQO4URijfKR8NIniyu8bthlIIq2ua\nw5vK8YLJP6FMHj8vX/nKV/Dud78bAPCe97wHX/7yl/+plcZr2FIcnPlCJ7xia5XAZ9X0DCp0utng\ng8afO8Phs2rwbtssNtF+cdsr27QRZvqfOtNxfS+1icZfAwL6kRgzQFmDYuu4kkAYlRL72dTjU9m1\noYDXE5p6G/a9aryyjbngP6Kd3LenY/O6sk+6rYEIqDkCVjpCqDA4f68RKTvbnNcCEOXzNhcgbGvO\nPTVwCNpJSMOcNGOUNTtYXWE6zGPPcQyDmJykmveMETssQtCrgrZgDbBmIAXFIh7Bft+z5vvt3/5t\niAje//7345d/+Zfx8ssv4+7duwCAu3fv4u/+7u/+CRXay56pZ4hQuqRkiLLwHerEbRIANRwmtWTV\nkUWijoEpxIrahtdILnRnBMmWhDxGqX4nnCTbVjWGRsfsWqZc51eS1RXINwukzKpWoRCvcVsJEvTF\n8eN1dIKw8QnpUxhCjMHRxLVTMmAQchC2ZJcllzxsTS8NhUx8LEWT2foak+1CM9YXImEL2iNH7JYY\nY6QjKea6+gTSeTLFgwYtCCbSCPrR/MTzDGNy8kx3haANwZAUSLeV74n5PvnJTwaDffKTn8SP/MiP\nfC/VzcUpLuyMHXA/JVM45eHZYkLdUs/eS0nitt1GCSUBuGcvn1Zzl5DpqFWG5zmpEJgB06HMR25c\n3Wv0kzKyCBDTktwloUEQ1cZLhwDic8gQ/0em35zxqnzwZwUsZvdDCFg/LTkS68rNwlCddhYElFUS\nIwOhWZ/bfdFWnRLdJmZMZuEyBuccIu6ZdXNEy7Xe3j2fVb9CmB6VvgKKgyje6YSaT1zQDH8mnyaY\nTt69pXxPzEcN98wzz+AXfuEX8I1vfAN3797Ft7/97Xh99tlnT9774osv4sUXX4zP9+/fx4/9s3+O\nf/Ge902DMIfKHpfQkkIIYHel1CIROA0VR0cOGKYJqK7r+gyeXzC9VmldmPM//Zn/DB+4/+tHkxDt\n3XMFkhjyG6TmgUw2au0flAyajBiyOh4/xyKiXDstJ2gdfStv/8/fgX/13/xGaDWONSSXLPaaLuCY\nFJjXUjhOQcxTmX/bX1dfM5i6/h6DCkRb+VHwYz/xE3iXvJ8NTa1WBJgPQ2H3HFHsqLNUnu0Lb679\n8rnPfS7uuHfvHu7duwfge2C+hw8fQlVx584dPHjwAH/5l3+JX/u1X8M73vEOvPDCC3juuefwwgsv\n4J3vfOfJ+2sjWP6f//gf8H+98G+je7PWw0TIAtjes+LGN2JoZQD3G1AB2jsVltHNP0Zm/6rrflxe\nyHAxe4UqtrECainRzdNp9X7g/r/C5//X/2liNjJ/3bojTpxkqqHbHJFBNlKEN5VtV2JIFxpcH62M\nSlhbYzmrsyQ9fVnf2I0zPvjf4n/5o/8RgJTsYrbZrjfbm0cbT9gvj3yx7yX38knu7Yt7WgvUIP6Z\nz2q9QVqP6yG2DkxYDshcpzPitCk2OiN436/+S/z7f/uF3FFfBC60MB+SzcS1YJgFRAyEwGq0SEFD\nKD4AvOd9v4L79+/jVPmume/ll1/Gpz71KXBx9V3vehd+9md/Fm9729vw6U9/Gl/84hfxlre8BR/7\n2Me+q/oDVBQclV4v/3wStqXECS3grxZ5ZJND17liB7vKA9P2NEeEOQLoBNCp/iNGn9pTGDAEhfgu\nAxTmUBQA55rM63DHBwoR7OEnMG8dIlR3fRNamuOa2ojwmnvsikDgZ9bvlTdqGCdYYWP9PhmAymaf\n0TyGdgDSs90iWfdO+9qXTuRcpuEGWy4Vuubab8j1OytRxHgo4eWEEMo90aKMllFv097OVmDn3HFa\nqBx8S/mume+tb30rPvWpTx19//TTT+MTn/jEd1tt4aS69uIDNUVywCcWIMPtoWIySGoeTnzVfmHH\nEeNPTD7HVFabUHHMiN7yuPfIwVMgG+buZV+2naGu6fXrTWyzuqZL/wiaC9B5VFekAZyaEBA9Q+8A\nHdyTNj0c8HERHn7JpkupOMYvl7TjZ3fUqNhapIr4MRvO4CVZ7wz/1JCKV2GK2RbYmR4xY4DNthti\ni/UFM3h9dB4RDZT5Yh0gGtgNv2qYGUCm7I/r4TEykwABjgXxXM4qwkWd6arIuEmr0b6Z0+HltdRY\nUI2F3LplqDIRKwyXOMrEEZ6hOlzyuaeWBQIKlgYJxDJNF8aU3eTEgjglfoyJLyvYQyDigQTibFGF\nryZjbKPuxMZuJO067hhQET+4pDiIouWm6ea0CCXGlksQvqu8MUVj2GUIgadQKJNeMbxMdlucrEHk\nXFdwam0bFnViG6mLXajAhs0TYg2gt9IJAZrpdVUGDJSBKqxoXsq6QK6Z/YDhjlykJIm00wLwNdN8\nr11xO4Qf2YmiiaarlTGMlF4JBUEGI4NwZ7fXOQh9RI4Gara36tTkd8eZwJDQYwdTeErYvg/Biv48\ncTuKcZITrAxo52egVzhZKgwZr6lRgCSQ8MpKXeh1ie+7xYkUwv5T10Kaz5VetWcypx1Mw1TxmhLB\nN8pyKWGIrYnZgn+mGDTYy75qzJ9KQxPGg3oeGIUJNZdIbLtpam+nzM6sMYancBUXqkkvjmRzTBUB\nteHacxZkQhmdE1rp6pZydswnN3wwbTByHv33ZAfm76yLpzYq4fInnFPea+/T7nLGQdGMiljsHZst\n4s7MlUxK7cijvwLqRnvmds2oOn+nhuNBJSTG4fVRY4ZPiNqrDpoqPHWz31/gmSBWYWj7TfLNx2PA\nc6jAjyGrQsjhn0imWojoEsSj47yCmEqOG8dmbBhoVn/Z7sW67ToXrrphMHUDBCojdudXxADAonjQ\n0nHm9aQG0wn6Q6tnOLqZAyKUH4miTLBVGJ5K41FeeuAMmU8LBWRuFSqBAIPIN+kEADVgGVA4045y\n0IbHnwcXh82n3CFAKW87DiI2c7cnj+2dtF/RVtmG+f2kxbxVsTutXNOL5BRHBCNwZcI6Y2QJYaIu\neVt37VP7CYU2gIEHgfZs12oJLXObzBHW4gveFE5N4Idcwhfe3XvZU1iQCWsRmPDIjajusZYMFJDe\ndnMMn0efbwVAOA0XXm0/foTuCmYGD8YvTDq1TShKT/wWU5jzzWABu6PFoZ/EYLlYebqcFfOFVCJh\n+rqQOuShOg9uBIrdVDVLRas6DxgwGcZGTJLQxmEpmZ1hWNz2MzwEikw4xW0qUHN7TgyJBMbUzLNx\nr6iCJLx5fm2kaKcLHwn3CHviedREKZa8moExqJ1mriCTDtDz6XGfYpmql95yTAqUtQXlgabGBF26\ntdsjYVoeCxuqPpBJ5qGALefQDl9yM633i30x7ToiCmiQBopgFpgQbT3HmAIJMCQjnRtwCZVTZx0X\nrXKyCF7Op02CuhBKj/aJqko5K+YDEJoOPjfWP3YmGa8SVZVKVgfitcJD2X0PIIzv4BNC1cJwmwdO\n2yZQ7jB3DThyIiJgesd0xGLTemW0iQSZkprajBsy7UQjNT9Cb26rFQESRkcSEuvNM97ttNiRLtJo\nS2EJjB3BqFrOlGXpKVwIr3x9jlMjhvFBL3RDj/7Q/oq5EYBrkrb1y3uitl9RRmp2CgOrl+Njzhc0\nu765Noz51wzvCg1JL3aMP2Gtj0OghEpvnKp00M3H0+UsDFU0z652an/ovpwV89Hg3q/P1TKjuKLl\ndswDeDA0N6kqipElvtm0MI6WXcuae/G2rQT7FrZWVUvGimzzvFWI7aKG5j8JO8Pdb1VOJogI5vyY\natK61wRTlVH9u1YIozpW4jqtGgX5u18/tNTrGqn3houlI7QhtYgI0DThrsN1+OlEgjzfIeeY641K\nGQtuAquB2RRYnLtkEBuPaQlphyKCfvYB3dmIQBOAOAw+RWsh/Z0+OHH2fUSzVARiuSReVTkr5lMk\nNOG0JDDB9H281/mb+pn2Ce8K86YM9BSJzwtdMlrECiP77arIHekaI6NTEnru4cbkxfPOhG4iLPLf\nWuvRLnNmeBo+r5RLA+ZdLLunFXl+hKanMo6uD9AbUmraFaCqGC2zlwUs1YbeG+5cXESKBLbWnjEc\nKZRZKLvMYzc6n11MB3FmHpqD1uGs2IE2Mool0YoWZvFx9/wt5sX0IAqfs+FHjgEWPZMaFpG1nAx0\n5LlGalFutvWPwXAR/hb0piFIj9l5LmfFfCz7WEOgKq4ySIWzktFyH9s+AeyE+8tvdVBjfx4zfe01\nbfmLSeZvQOSBqX0J6c9FWv/MgGJrtTE2T7myXd0M0dK4jxtMe0SmMIkRGSYJuZ4TF48S++EkodFL\nS2nghNRbw8VhCSbhSKtiOoyTJcEeMsi6fBfPK2NPoRk7E4baUoSnKwwEkJWl7Qz13fOwg3OQsbxQ\nW4pqsYHYvNaZsY2blyWut17OSiAgipalsNKecdTD1xvs5KvkQATk2jNata2wlzIae6sGNZlVnL/v\npZRqwEwyXtUMlLI1/R5QtKgCxT8b/WgwhmwCjFGhWPaZGrTJfLAjDwdpDuN6EzQPz2IolZN4aGAS\nRaXT1Fb+RC3tJjOi9NeJjPcZ7FwSphPWKrCoBHw1WaUYNcpFJjDBx2NUMuV8B/rw+RPfBhWHmSrg\n8as8Fs7qt+sZ6piZzdgIF5YusJKPCpvtIGs6h6R+zAvL2Zp1eMWx8qO30p4Z8wGza5peqtmtXxe2\nTzMfiWHUwxBpbAvj+xCn9sBPXN3GGqfx2CEnY7rV6jKZSrswjBLh2XG+iAvFQtgF29TanfFMuxkJ\ni8PO3jszK3iwstt3arlJehMsheFo+whkYrYU1CVsjHBNBDxdlkghYOpeoDkjQYBDa7hzkLAH41BP\nANBmG4tDCyIodWixqx0+q3quTja1QNKAn/6Drd9tFfZA3fbeGMSiDbQZx9gg6gmYdjsoqhNK+F+M\nW2otpqkISAliieLsqVpO4IHXTr+RokYh5ZzDU+W8mM97I00sMBdwwshsXfE+BgKBtNRFLIGD3V6i\n9MXWpiqjjjEs3yMJ0JcWUvs6rKwL5xWy+VzUvZl1oq0umhXN4SKiHnjIWXfG4g4ALlw3tcj+hfBL\nkNpRqBnFIaZMXtCwRaYhlqQaZ8rUXGS8vGPQ4XI4pM0ngjxU08Z027bUci73xTgt56u0KcyJojZq\nIEB1vMUueUcCdo7mgHLJJdWW867D7CbloMzZnJmEFZnFBRmnNXd8sPo52DwrqK95sMo+fHBfzor5\nQqL7wPGoYrPjSsxhhYwFHgTkq9K/AVJSNGR6+Sx5AlFqU4ExwrYi7MDY9uJ1BzGWdrEt/k3pV4kS\nKc4UEVvA7o1bZcyGo/ZrAiy9Y/H06aLMFubwxg8tYd8bJz92h3uLhF5GLqyXVu4FSiAE+9x7x6U7\nXIYvjg+fj6G266CJnd1gSzNmA4mfbcGDXRL4SkIc5ZhKeabDxB2HGGk4bEbmbGlN0rzQAR0t4y19\njCpjx9kVPIOM4D9QRYs5mgRC6cMsqGYmqwEPt5WzYj4rsz2E5rjfIQ8ZjhKP3qbcrKwRndFaM2Il\nHNQREfT8zAGlVqNNUKFt5DsZZblBiO1RpDlyV4DkZWQicelPyS8wiNl7R3et2Joxfe/dQrtE0HvD\nYhgLi+9tay6lgkCoFcF9bz6cekwDNTImj5auTEBNb2XpDYfDEijCfrNEVOu6YhGBLovZy850DJbo\nDViHpRMcWjIGONEPkYg+ssAbGyEl/OMPoVNjUH09cCsOEPaQQljLd4SsQBwDF3MnZV2upp2oVIlg\nzil3TJlswljS0KO478yYT6ZXy6doUQxN1Y8rjl9nGIGUj5T8OlIjAkhIWsZEj97Y+2mJIVz6BZHt\n4Cs1YV0k5/exPABDTqYB7fveGrpYBIl9duaThJ+9GfO11nBYFqiO1Ix06zvx5LkGDqFCBO8hVzpZ\nMlwuR3F4SB6fcXk4RPpDao9tqDuSrLNbE6ybYEDi99QeAhkj1jsNjBiz00EcmCSEr4+xLyUY3G6p\nB5WnJ+U4xBQOChKL8ZxnXKMfGJsFbI+WzCO5tME2xueCohj8UOUxEUbD/vyL43JmzKdB1CQQ2ktV\nOu+JCZqaa3YcaMDJhFbHz4JmVuhk0OoRnbcUCbUotTDhEu2YgDWZcl1AOEmPZ3MN1yJ8qzVBh4Y2\nJPw0zWcOmUMXiBzMceP3kGlEPLrCGab5UV0chrocMa2BVi1PeO5MOVTRRXBYOrhAv20DmwgOi2Ab\nHatH/TRHk5sKtiZoGzfn2hispmoDKfi2REs4xPFjGynoVDOihvNMeEisE+3Pvtl6rEFk03htForU\noj5f2hxugrATIbgiMgYku4QV/JhWiwIt2PtWaj8z5vNCBkTB2PGa9sBsmGvYPcTrGMP3cCGHggzi\n9UUmstgpDvKlXToK445yr1apTs9nOtAl/tJG7ALfJQAsnSkZgEPv6N1stS7AsvTQisvSw9UPMmZh\nzurN45iZb6IfaWAEUdfMWhIdDlipcG3YLEayNRx6D4ZYeh7U0jYTKJZq0d6vQ9E2YIMdRpOBzx0i\nipWbkD0NfCvtiiYlMWQMpgicsjFkc6dUtx4oINxIW+ZY1eM8m3lEOY+jKeDxoTadzK7WYMmHmT2u\nOlmynYmgCsQPuAtM6RFuKGfHfOHFrAw4dFrnowScPWI1NbqPRmsRawgti8GcHKU958w3baLEdH1d\n7oATKDUyz1RgxEQeMGIvZospzNmhfq558yUEt/VgmvLQTAv21oMJl6X78Voa8JOvNbcJ206mTBCe\nGpqLy1yvdF3h/KcFWnpelabozTRfHZehDds20EUwtGG0tAFlKHpXrOuGtm3YHMoofI+iMyhTuVM7\nVuioSM0YUHiMcBapCmQomlhqCoqenCevewy0ZUGoJ5CoFJAQ18ipzjGJ4ISwq4HMiFeWF9joQDkJ\nP28rZ8d8QG7roRTPpYVixiCZY2jd9En5UyYCiEGsDFVPjA2YKjWCJrVe5GeZIJq6ZLX7m7TYQG1z\n5TYNHHa69usiWMS0BNf3mjPdwSFm791/b/Eblx/sN2O8XrYNqTKlX9rDdEq1WIAy4hhuO9a+2XsJ\n+DQaIVyLXQ0cz6bWn6Hi50nYvMnSgc2P9+7m7RSbINNerlm6Z6cOJBr2QC5nEDmA/Yun2xxt6woF\nsEjRgDuCb63ZAnzpf4ajKTrXiEhnIVVzfjkGjDaqULqin9CQQvv7dbSlKItO7wjhkvsIM1kExyVt\nljqgMwPWLGKJ63mNnTiUtmBQX33KyO0ttEuopivTLb0b4zXB4rYfX+ndTMcLYonBGK/H8sLiGcD6\n0s1u7JlXJWxQbuWhs0eOHRJAsetc283nUsAY0EOzlm6kkmNq5NcgGGI21rpurpnUsrf7lGHdDL65\nUyjOaJJOyQltdt7fUM6dxubhOu6qvr2K8xdwMTVz2IKuuac5T+MEZPTwTDqTpx/vtPYSeLZOCuqC\nduqyDU9ruqmcF/Pp/qNCtxEOjPg5mKPAR/th1oDK3d+J2Wmww6UrVCcJW0+OtROHaJ8kbOX55LHF\nxMUytTTzrTBWk44WY67Zw9m7vbfrDIY2d3Aclh5ah9CzByM2tGbMiWAuFzBF2+Vr2SVOd8CY9yOm\nbTtiTHUMLL3jzp3LsBN5GMw2fB7E3ssCbH4sNHldVli0NGibGqOvrvU6Jza5wNP0M/1HIQix3tm+\nuQ6IR7VIw7at6G2ZjjBTmPCwPYZkjDJfjRrr2HkzxReHVtNoB2F6fPSfap6buY7jcl7MN4HylGoT\n3kkOhEAj/8YERbXUx4GCmt0HOmAS1vKJUKaBGEmY5T2mSanexRQCrFMEsXRQlwuWZjBpWbovnjcP\nOTMGvTh0d7Qc0r5bejAfc1Yy32XmxuwutR2uNXGngcyB10VCDyBhdgg4tUVqBh70jtZtiYOnLzGG\nklPSe4dsG+y4vgbxPZC0ORWrz1FzG5FAgkLKkyINZKKoPeMR3bjQ7cyXCTKgQGUAsWWK64B+fJeI\nj5X30ulkBB0UO10BO8lM7Jw99ydAt/Ai99YxdEtb0J9JT+usbU+XM2M+FIMOuR+t2G40ei0pjsQN\naZjvqtJTn9OolqjTrpn38c0JkvJc8WRCxYCIM5A3hHZAdzttaQ3L0szOc4fLIRiPjJqRLJ3X++dl\nyYV12njNJX++ppabskiTIYtDJu3h3J6jZSC0KRQ9xr23juWwGCJQT+7klQ0dkNaL8Bvhk0CvSzSA\nOVwE6lVvOtA8N3yHQHXLXfI7wm3CCE4XzZtpPAmf2ghnV/RUMjStS5nkwjCcRXXzgI6W5vbOhJlc\nQ9O2nghsZ+89ivGAs2O+ul1FA9btryHjhQMKRduV8XUremcbFgkfcMdtjHK2XvVuVkNc1ZiHtUlP\nW4PPDW3q2q81X2JwxqMNR63U3ZajdluWZWK8xRkulxdc4zVmj+4BLXPdr3o92Z5c57OMBy13e0v3\nwZMQZLS3psX8Kgi7QjwXzNK71yvoPu7NtVAfLe03j2axZBHAyjlq8ENGuKRNCJ2otICeuaiGKWCI\nQFAMArNbwSDzpBOe9FuqKUKloBkB6JelMtjGNkdOxfh6A6W094ZyZsynIVmOcmrWgSPLVQhavFYB\nD51hGHUejA14ang/xrghHA9hAxGyltIZpBs/lJ1riUanJZ4m5pSwSA5Bbz00Yqzl9eavCUX5x0X4\nFhEwReNBPM+NOOO12DaTY1f2E7KJihgvRnXY9xJDWC8m44tkKNgighXGjJYvpaFzK6wAqg3d56z3\nHsxn4BPoCozuXmyjYIw20FSwyezptmbYDvmYQOhRfiKm9ReROSGTe2RFKHQ17D2enFQcAZnGIkLO\n5thQjgv3S4ZnmdUIpvG+qZwV82n80QgmBCEwKBCUTEpgIPMARp2VazXd6nQacODDdqTBjbIjS8TT\n54WedR5PyJsQD6CbnDZawMGWkSfcJEoN2H15wc4mSIeJXZtMwgRKySQuZSm2FZHHMqExUiBN0EDD\nS6uFmIJ43L7iZ2trCqXeuxG7U3ljTCd8B74qugoU3RflgeZ78WiDNXfA0ONr7n9gawPYCuwXW6Io\n1O12WHo6j1AKkob2QdLsKOcrOl7oRgf7nXXYdrGkS45l7EgsNmmcB3FDOSvmAybzKwbFYEcZWH7n\nyq3GashEjP6nFnmPEs2Si6nwgy6HwwdbA7Id6YBuRmxJpAjtDEhs4oyWSm75sVd6Uy2dQRf1tT7F\n0jyipTf0btdzOaI7s0ZOzFNjFc4UO8CghTZrM8EJtbVZuSLq2gDF9kkhFdpQBaIjNPRg0iISrK+h\nAUxE5fcq4h44ilmaw76hGPAlDLXtXJzv3gTaWyYiojOIUSZ0ZAQFBCgNM4OCRACDv3FtllgKKEzF\nLNtpBdZ/c3NsFWi8PIQgrw3kfzvwPCvmY/MnGBevmTOkkHoKH15apbsLaW6FCS1Y1vcYWDy0Dnca\n3o5Vwnulvqt+gsUu7ZpRHcxa12ygEObFDea1FF9q4M4Gho7Ro1lwVWUmCpmMcMkoFxLg0dqeFBJR\nE1zNtdOkQYPIyoV1KqjZ/XG9RBGpCwPV1NyqijYUrSn6sJhQcQJtDdCRCGPUPoZQkNCsiYrUbdRK\nNT5rqp49DMGbERSB/Dw5RKjdCwNVp0x8h/13ZMasS8r4v668nRrUmpKsaj2AXrO67leghBQmRFk2\noLUedRzD15By5ftRtJoCvuSgoSWqXJXyl5ERDOiFaaaAniTOcnxWDRXDMVSCcLd7HtGVTpbCZG2+\nb7I9iidIigMpe+EEyO/EBAnro3c5mFuo5p3JnGAHGc/zsbTW0FWxNYOZrVmC2aYtUsbHX7FRA2on\nop6QDfs2PMxMxLXzVvLk8B5CciQp1Dqm4OtCQ/5jMG6MBSp/ytT/RoI5BVdKOSvmK8hnKiGFvQwy\nT4VjBeubRvM9eFU/ulTkJt3pUVoZr9oF8MnmSUVjel6dDJvUgboEYvTZUqu1mQn5PmuQaTeE1TE7\nTOK7GJvpl1lLynGURmpJ33aTKoFDAXr5SFzNYfgAM5KZpmM7GjWfh9htznjq637sU+vNNKEOCLcc\n+XNbEzS1a7qaA2ZQcEa7vY914iRhqX3jgpOB0xqAu4yXpiBtHLdZG9ZRk/w6Pqji9Lz4Qv9pYyHL\neTFflEp47LDEZ4Eb2yMHPC5UbvgcuffKBz9kvGq5pRJcMr/WBsDyvZyCEWS4CX6wfhD+pQ0Xf8EA\nBdKgLIjnz0V6z5p9bkVh0J3UrY6GPSMTSk4KsBAoysdgFEnm56A12nStbhm297bTXFLj9YE2fNe+\n2pYi9rE1Q+3SbFNuU8Su+bD3VCE7G632NURZRS/ebn+J/uUyFZdf3LEywe86WLPGrNdUpJrLOjeX\n81GzZ50AACAASURBVGI+n9SQOo9Q24SP0prvaZyDhDm4FrVh9hr3u3kN9r87YuIIMCef/Tl9FjqW\ndgcJL2me7ZagC8HMTDEpzqyx0M8hqLZa0eRHTKdwW8ieaUSD43ETMq4jBak/F4hV+bYQataRfHlK\nI4SAEET7GfdIjcOY1QbJsXAear6EEeME06aD9l9BO9R0zaNnywTMjfI22NJBZoNLIZydotmSGjIR\nQyKFGCFwUd4+07SQaYD2iGNfzor5onu7wQsCldm+YkoCNwTNk7bPI7kzrjm4HHD+bYzjDMlJYtaM\n8SxtynCzqeVxP8DlCUw2jF8Fcg+1o0hK3WA0QRBttf3smhQKUv5jU6Ryq1cWwzpp1DLokq1Dgc5R\nH18dtjcxAdXUrPDwvaAwEful8MRYqfkNZnr0imjUm2PC8aiwHKi9Bzg2knO7a3fVgBwzi/mUIpx2\n9CfTiO2KgukW7Rm7rWg7gXpTOSvmU2Rqg5S283peajOEpNIxYrF3L2w0R9KI/cQeLp6jXosJNWeW\nvf1HmzHgK7V1pkWYYKMTte2YTh1CIkNcn+t+dH/XtUCGjsXygjoDn2JuQkzsNGYwHrUg26Mn7e1a\nzOzjs7QoIrKDwcaxFeKH2UBt2FKI2bi+vhh2ZQWq/ixnVDJySF72rwiWgIGqaN3hL5GKlPsobD2l\ne3wuHp1swxygzftznNn/HN265PRqvJ2vMqv896toDBx3SnOA5pCveo/v7Zo03ExydYBoC5Chc1G9\nSkDBsQR1BtvFe9b07Xwy1wT5F04eJ7JgTmSqQBrqs6dvZsYjxws1ZrHpIJ4ljUwxQd7sG/tb2z2P\nms7/7ugoHT7UHgkV6w57tiM8vJLXtNo2lDGJ+riHM1GICb08oCacREoPM9Jr7N+FQi/XMkNBGvp7\nbXUkxY/ocDcgVoe3NyDtLeW8NJ/OUJDMwQ4n9KOULqTCAXGemRDUVGeN20NAR7e5Uw9QQjpjsv7h\nWcwUGkm1uLAshQjZNJvgkQRPYhSJXQncqdBPMBoh1fw+U9u1eI/ofzBtDTWbX+bvZCY7/pBrgLtf\n/XpqPNpmPjPRvyGV1efCRfg5yVKZAN4SDh77fujwNDBM1S/mnPHnc29lg+wiTNL7GGK1MJGw7UWA\nc1hID1nVbD/HODnTWVtuT5gLnBnzIZiteqH8l0nazFqJxBnf7X9GMuAk/eosj/wNlVmVkCwhCyUw\nXPq7pzpss1nS0k5JDZCJcVvRbH7McdFauUcPhUC9i/WauBaxY7uuGVZC2pdjVzmg2lGXVISCQhFj\nONgmNHBHQYXhEwNWaVTbTiFkD5o0b74vNiByDqQEhVvKwdR4YwxIX+b1T51fk7eP1dORN3M/QGVc\nAhW4BKfwOb7xuJwZ85VSESSKKgclJQJW5O+FeZCMkF/vOTJtA2pXyVnJe6j1xjgSCq6MgpBaVbmE\nMn5/rPPJCZhJ7cZ2F+hWoScZSUDGS+hqTJvMPGu2Y4lEaZ19ya1GgGnV0GRNLBpF3btcAhCOILFO\nL1H3BHlL35p41JLUtUWrgQESYQYUgcbz/Xg6UQ2Q4PYuxu7qxH1c3/TAaml2vnz87BpwF5u5F1Rc\ny4vU9NPYPoLzcG7Mx0F2vJ6l4ELMWctiUE8wi+xHBOk5FZE5XWCx/agPA264xM8FX63nZBgjSEPv\nbHraEoLcyV5toUbIiGoTwbVpgZ3VflLYsctH0NS3EoUGTSZl/YRE+xLDMxGWAiruANXo33Zs+M3Q\nTThPmd8zmWenvTlHgDthbCcBq01txzYg60Fl7AygjtSOpA0dsIxpMNUY/KcAHUZjQBuzle1QQHlO\najNJKFyGgbSWdvSjme/MHC6JxYPOj8BIkdA6/zpBxQk28rcKKYqDJrRnVOTtmJcXJiuaBIJkkoQ5\nOv3Rvklnyiz5QzNp7c2sUchAyWzpCY3UEmWXew1bQxCxFDh8opD5Q3vOEpzt0D3Dlfu1DCR/q/Cd\nTGXN8KXs8pw6ZzP0NoYpkzS/+tdxHAB34vt8kkFugpuxZrzTrlPfY4h2SKVON6Hxo7wtODfNd0up\nkg7Q2OVOTTZ5rnTueg6s/cwwsUlLFpxb6amRUAqWSqazCZ3iNSFRV52sgJoo2r1cF5Omlay4kGzN\nnOOkkwn3ThnZEX8OYPakknBhr9QjInYMVxHvYZtaNtR8jjt26kmwaceOWTXUWVSASZBSmMXjcuya\neOy2QJwB9+2Pz56ZrNro0xHNVfsCeX4gr2fbLKVaKLoQCGVcJdQfzR/iIAm6vK2cKfMJuCM1nByh\nwchAGttZOJNV85EhBThyDw8dhXn5yMT8/iSfKFg+z8LN4TUrWqL5GqIIfHe31VkZk0yWNsusrWlP\n2sZeWw+j9w4YnpzI0g9W7VIR0UQcIjkWQZ3OYJXrdsQcvSwVky2bCIavwfH6mhslCTE1fJPm6SHK\nGQk5bQkRCd0klZwdGSABfyWYfmZm1mjmwYh07fZngi7x0z4weta4Vg9cYM/POFoP5u/lmKpXo/WA\nM2O+fZOpG6qspk3F3wiNqrsXqrkOJDPsiUnwCvJ7Tgq1YQ774INq23zibE3K4EqDpVYPWIkC1YRm\nh1/t9G/n2inQTGtuPhDi3w9PqReM5fTbVGM/Y2yhYRba2tydjRWcuKPbCSnMur78LmE1K8R3jUT2\nv/jbhnrSIcGmyGzTrZkgs6OjUrhxMCqqCFtTcquRhcJEK4BdjGeZUw2E6i2mUNU0BaJfRbBzPhF0\ngID81YRJciiCLgZrXtK6qTyS+f7gD/4Af/EXf4Fnn30Wv/u7vwsA+M53voPPfOYz+OY3v4m3vvWt\n+OhHP4onn3wSAPDHf/zH+NrXvobLy0t8+MMfxo//+I8/6hFHRYByztysvodmqofUVAkrixGRpZgH\ncxZvklFKQ7YgBnzb8mgwnwCecW4TxcQ9tkl24W+wE3qqfad2tWVwVvUEbX56rtiWlo5mrWqwk2x9\nojsdMh6dgU2xQGGp+MTuE+9gs7py7AqhIbUgbZeq9kjU9mHEDn9m8LL0G/Mrf1uHYmyKa996Zcdr\nE0dY3xUCleaiapTRt75CNdJuhPBiWJofoYZhaehpy0UvXUKpujPN58g29WYWvDmJbnrM7ZqW74cJ\njumIsUpTFPRVE3D04p+byyMdLu9973vx8Y9/fPru+eefx9vf/nZ89rOfxb179/D5z38eAPDVr34V\nf/u3f4vf//3fx2/8xm/gD//wDx9V/VSMeYqEdkKuGF0L3NTymTlYTBomwYe5MT2Ez6l1G7ER0TWp\n/FvCnyRjNcO5Ud7bRHnbfe2O7bTMzhvWbcO62t/19Yr1esX1umLdVqxj2N82cL369+uK69Uyqtln\nq2Pz66K+YTlMBnOdhg25IwKtr7s1TdekY5gGs3Qb2e5rf/Yan1dcXa94eHWNqytr7/X1iuvra1yv\n11g3e79er3bAStm4fDQnVNKpAnMt0OcyD57JewKF+pw2SUST6KNF3pY6BBW2c544JjXCam4odoxW\nR7iaGLeXRzLfT//0T+Opp56avvvKV76Cd7/73QCA97znPfjKV74CAPjyl78c3//UT/0U/vEf/xHf\n/va3X0UzrFRhkbKPDJgXVWIZniOS8Zk8CjrRlpb6yODYMWgOVZo5OYAZjZIT3yiRRSYCmVpfvjMC\nHti2Ycx0veLq+tqZLhny6toY7eEViXrFgysj7odXK66uVjy8uvLPV7i6ujKCd2a0DGyZhY1HnWVK\nfIfJhfEwFLrZtRzLbbCtVtfV9TWurq5x5c9+8PAarzy4wisPH+LBw4d45aG9f+XhVfbLBUUVIgxg\nnwWCz2+xoScbrYzj0TJLnVdUS5Ca0X5vjPZRmFYrQroeNrr3kGcSZkbuzFFINVXhdG+05ubyXdl8\nL7/8Mu7evQsAuHv3Ll5++WUAwEsvvYQ3v/nNcd2b3vQmvPTSS3HtI0tpvOyk9WSPhSdrTvO3v4eq\nvyAEVuYMaIbY0eRqQpHeG1R9Ubn59U3Cw5egCVWcukSW0CSbeH4Y39emXNRqDesYwLphbAptA62t\nQRRWHfO5eLqJqzYl3V265dXchuLQBX009K6zwKjogf8GXZmNzFQbPPhl2zZjoG3DKw8f+vuB6+tr\nY8wx4p5tpBMrNJgzOBFJmHYcX4cl8zIKx1TDeqDtR+9pY24YwLVZ7aNrP6RmCXs5tOTUwEh3EXAb\np8dMnHaqXtubz+kgxCPLa+5w2S9csrz44ot48cUX4/P9+/fxz/752/DuX/4vgzmspCRJwpjzau57\nKhylkEKagbSoMAKAe8fqaaM1tApIz6o9a6TUg+9P4xaZIBbgJ37mZ3MDLaVrZfBWtaWULrsdMk1x\nSnx6TjN7NTfr9jy7vQRrg9qh4jSOSQ5BjoM7UQjjtzHw9H/yY/iRn3sXtrGFLZewbJ5XxbEzYn/F\nCKFIhw2PmPYU/WC0n9ucyKzRqow04iPmELyUrxJrrxbobte87ad/Bv9V2gURAxuCyGsTX18kLdU4\n2Spj+dyQFLt6AOBzn/tc9P7evXu4d+8egO+S+e7evYtvf/vb8frss88CME33rW99K6771re+hTe+\n8Y0n66iNYPmP/+H/xgtf+D+y9XzjE7G5TXO9rsgz2mfmS9cvdhpyc7dlOUtPJGyasa0A7BwCwhKW\nbduwXa9e5XDYCT/OS+KYr6X5eQw+6f/u8//z7uShlgTBQ06QUS5jDGx0bhBKB0sySqbZOQ6Hg53l\n7nVfXl7g4uICTxwWXBwWS7y7LJZvk6kqwjiCj0PCrM2zdBsEHthWg8UPr67xtl/8FXz1zz6PK4eR\ndLyMYr+F1mqehZvEWhAAUPLqwATn5sy0+fe0KTc1m3MdNs+rKtZ1+DFkmx8fppDWw65urRtYda3a\nWkOPpMPWrl+V/xr/5n//32wcmp130XeJjwEm3+3+XtCajeO8SooSWWTXBEr1Dv8X7/9V3L9//yQP\nvCrmm41O4B3veAdeeOEFPPfcc3jhhRfwzne+EwDwzne+E3/2Z3+GX/zFX8TXv/51PPXUU68ecsbD\nypviAp6iKvy7YgYeaVjieZtgXxvynd8UUHu4aucTZFRL2BG0EUGI6ZEQO5tD4Fqo9wnqQBjRgYiG\nUPF9hLAz5+iMs7PLRwiFqu1N4+WhmQE7l451vcS2XkMPC7YLY8Rt25wBG1rJks05TbtwYFs3c/is\n7lS5NtvylQcPcHV1jX/4h3/A1fVqNqHyNKEcezKeZWEDRLljg5ngMsUg6+Dcjpj3akPpNIcVB4in\no1DZLWarnSXIpQ3acTw1d7SZRlg4tscnOekksGwtMnEmNa0U+rPLzVc/Ci2dKo9kvs9+9rP4q7/6\nK/z93/89PvShD+H+/ft47rnn8OlPfxpf/OIX8Za3vAUf+9jHAAA///M/j69+9av4zd/8Tdy5cwcf\n+tCHHlX9zaXYI1aKTq/W9Ql4Zt9yIyVtimpMkwM4gIX5avWC2IfXW3VB182fdauKxgI7GbFJwwxY\n/Jw73zA8op2IdgWTR5d98XjbsDqBrdemAbfesC3dtpCPARmHzE8akTwLFlU79KRGdIw8l2ILr6o5\nda6ursyR8uAh1vUarzx4YBpx22wbUNEMQtgrORItUtqTAWKGAAXGujnzeFsIv8nIbjsxvTyXlgx1\nlIRVPq4VuLfCAGzRUMXiGpKQsxBNzE4NLMg/h9OcWzI3aY2oIvpACjjN7CyPZL6PfOQjJ7//xCc+\ncfL7D37wg4+q8tYymXul7Jy5Hq3O37I0jzBXnQdpuPZTEazVgwUYbBkrjR+rxz00MelNpogatuHY\nC1fts24TQE9DWb0c5dkpaGo/fAtPU2Bz5gMiR6bAD3URYKzAKte4EqBD44RcogcSrBRmrw6rQcfK\numK9vsZ6fY2rq4fmSb2+cljuSxjKWXLb0zNtS7OTjGY7i4NlzDEcQqJZGkEt8BNAOIhaRLWgzIFE\nu+t4R7B7jHtazSFGFSZ8RxGUwSwkKjk6RDPmrSEgdPR+hjb5LNJTcWjdVM4qwoVFgH0KESq54plM\n+MQLWnFTx30csCah+YIurQbQjUyGdVkZ13YyeuMJPeKpe4K88q86RXqLrS2QYzkY65ItzxXv0gCP\nZWRmNmkDrS/Z9rEBY3OHDwnOtIRpso51XYMJLA37cKJOTp8YkEx4bcsJdkrTFhuKDUEYnG4QSG/o\n/RB2VSe8JRSrB5CMzF9q0N6ieYZDeNQtU81TCzK8MCYqUQG/V00CJ1JoPumV7qNuohw6ouJ1npNI\nyV8YjhN9xGvqWrHAznm54eZylsxnwnI3gE2gw06isY7aGXL7dbpSBQBMNoEZ6F63ZtCszblgbIgJ\npAeQiVAj6JZar8xLMKwgd1FLas/anuFRFHQYWMO2YP7NfPBl86qRnIqfmQdF7wukdXSxJEYSOdzp\n0NgwRrc1Pt8Ya3/HNk0eEGPab2PAwub5R0nI/jqGaaih2bfNt+tY7haLUGktITw90nEM9W5MiAAs\nfKyE0kna0wQlseSE5AHOSyuwNSBsbNWqCq1GTdnyT83ZaRDfDvjMk6DcoUJfBAVqQFbTqupa9pGc\nhzNjvqrOjW6rPRbYosAPAgzCEoCabF6C0HTaOFNVdmVNkz0e8AZ5L0wLtvKsaWHdpWgs6JZ+sZiX\ncHNlYv0zx4oEnFURdFXoJuFyVwV0bHHeX4MCon4sdPe6R4wZNcNMA4TVs0RXaHgh7a+49kGhAI+m\nsZNoIQ1YB/piIW5sh+ooZ00017Qa3NNQgyHUEYuE11CFCZaYhdoYo3lWbB153BeA2LEQ9icZJdJr\ntHkMfP4RwjTY95gY3elT883M8JKMnShMHLYFk95Szov5msTZdyZYXG9pSVsgMmmNQB4FBrhF5EYy\n16IqE/OBfhPTEbgdwsmh5hIwFyWN7hq2lMKiS90067+7hFR1gobFRG4eeDyA9GzCksuahjaiGQpc\nXV25tFZcHg64WBqWJjg0YOsdl4eOQ7N7YxnAHRVJpSTMloTMLGIhvY8DpLfNenu9DVytA1frhqvN\n1t8G56k1LIcl6lkE4ZHtvcfZ813gjhVqq+bjxDVJ21TblAwoE4E39YB0mdcXeb+ipGlsLZ8VSDtp\nYLh9F6uEWhhREF5XKUKVHm4KY86t7J8h8nrUfBVI3HBNUfOJ+Xm9a0ItkxM4XYJhhqT9tz+Ak9K1\nwnrs3vNzl4yk6M3W7arNZ9K8A1xE9vWsbdOI31y34SFlKzYFrgeCgRjPqV4/oLhzWHB5WPDU5QWe\nuFigBzvRtvmEZzzmZmPkdk1Ex/g5e3ACmnO9pFayetiGgQcPVzzcNmPAbeDBw2tcraufOAQsi50X\n2FrDnUUy8qbbYZ+XS8fFsuCwdLcNU3OEZmkCGS2cQtWBZWOugQ7KhIE4o3nkEfsiAaEGLw0kBZ9n\ndTuTQxAQmAzILFlkKiS91eD+eECYGo+GnmfFfECyXnWkBB8lBpywP0uo/Z00Cq9nqyexpvctJOIg\nM54eNUpG0dR6EdnCzy1PDeI5AdAC44bievNA5GuL5bzyWM91UzzwpREGTI+hOBwOELE9AaruNu8d\ny7Lg4FrJ+mvnJFS4GZK75ZmA4uFU0tIQIiCF25iDMNM9ktIXLP2ATTZs6wM8XFdcrRseXl9DWsd4\n8BDrttmCf1ccesfFYcFhWXDoHVeHBU9cXuLycsHF4RDMGWkyuOsefq6DemqOZifecifIvBWIGtSY\nlWfiMRImGPEEHxANDVU0ejlBG69ASjqlPI09PdZkZB0DaK1YOFro93UEO4Ei5YRrcmnj0Qly5GDZ\nfY7fw7azyR1ALs4mTpirqbCy1JVhU7vlBYcukY2s5+7yPNNPoNiAYXvAt23gyoOTH1xf4+p6tYS/\nreNw+YQJiqHA9TXWdfPtM8DSBOgdTz71Bty5c2Gbj6QZxOzi0R6U8gm3qxZJxwM9ev6e+oMXBSAz\nOLgcDgAaRlNcDKBdrdD1AUSa5UyFoC0H9GVBE1uwZ3D21ptFEKlCcOlpNQ4Fes7pMIbCEzTZuA74\nmQ7qiXt9uaV6ruFjEWuEPj9bCUU75ZirzFJ/Dx3nCIlHQGckWrETfX6Ojpn7XhfZv9+FkKjaIQDz\n9nvnxewNpuwDeGgY14aGS0nicg1GVLfRNsOBiJQPYiFM5nGzehszWiFhrCC3GxnsdKNcbCPtImaP\niRizkPkAjxHdzBu5eYDy1UNbR7u4vIM7Tz6Fw9PPYAzF1fUVXnnlFazXtv62LB1NFc/cuYM3PfsG\n3H3D09gePsB69SChWxeLZOkdrfE4MaavEJirKGMhOd6hFXtHXwZkVbRF0EfHAvU9cYKLi0u84eIS\nTz/9FKSJeUjHANZrQARLX3DniTt45uIAgeL6+hoPHz7A1WqLCr1d23mEi501fxA6W1ogCBWgd0Ef\nZidGEnJVS0vP45rd9iM04nir25UC9bVb623dxkRBan9u07szSHSgaWpkAlWuSbonwQfPkVI4dWhn\nA6obKfTGcnbMR+A5wcrqefRRMwOb+DrXauo+LopFcwE7DHBYCkrVQTjnURuqviCf21HYBinPaX5/\nN5Oh7O0rsNNtMVWFrsBodvBlBFx3i1KBNDzx5BN46g1P4/DkU7heV1s+uDjgSgSHLjgsCy6WBW+8\n+wze+OyzePLygCsZuNINuq0e87ng4iA4HPoU10lbuv4XYxtakQHaDYfDYh5Ot0cBg7l3Li9w+eRT\neEoEV6tFwfTe8PB6wbIcsBwWHDy+dGkNY2x45R87rq4eTjZebmCGu/D55+f8NfWz603rN5gHtkmz\nNO+ep6VCQ0rEmN5iTpCq+DoJVMLTimaEQta+ZyD40BrAkPQm5TnpAXx0OS/mo/VaYSHKIAb/SBqH\nu2t5WeTQVDO4664FpiZoLmrVJ6IxvYEWphPx9BwBzBCwVJx4KMFF0nkAj/4IeCQITxkcQrWGq8MB\ngOBwccChCZquuBCFHjoWXOKJiwuoDhwWI/67zz6DJy4OJqnVtxAtF7g4dNy5OOCJywXLYk6Ovixh\nB0lSXTHwXB96PGprHX0BLqD+2Qhv6R0Xh27eym0FWsPTT9zB+uwzeOLywu1CNW3bO55YuntUNztf\n/eIAYGDpYt7ai4PZgi5QWmuQbgJJFWhazzFUP0xlxA78wbVaGemQKR6TCTyOygyFBX1OtAoCEXAD\ndGT8dhg73BGGoZBeHCs7RuSr1sfdUM6L+XR+Q5uFsGPPZPO9s5SjrE+zN7H7BjJwrt2kM0ZCcgII\ne45atsiH2S4sGpn7/RYnYKiij0wJ3xfTFE/c8R3qkTJdoes1mgB3lgY5XLot1HCxGNFeLB2iK+B7\n9544PIGL3nFYTNtdXJqHsbUWr7HznppbSh/ExoLpDbUBcrDDLblofHFxwFNP3IH0Q5iCT10eIM88\njTc89QTWdfX0HoYeOoOoB7DIAbhY3OvZcLG4I6Z3HC4OWJYFIjN/RHvjr2giH38VzTU+EGoitNmp\n0rzz8Tw55o+Yz0pFIubwKl5MLm3s11IzWB+30yvOjfmg2DyKheXUwRRcVAfSIA4zV4p+0rQElYa4\nMlYTwGaaSzcj/sXDsGxhdky71RHMKIgsZS51efJsCF+HnQadXEJ2oA81W2dTXF5clERDbrADoYlZ\nz+FwcEZyt33rAIbbmAdc9OaL2uLXGZzd5wqtmbFnqZxe2t5tS4yoa+1mwuPycMDTT9yJ3CtoxkSX\nS8fmu9zNrhoBzcwBYdEyTYClu0buLhR8S1VvzRWUL3M4E1Mbdx8jAymerLhp2NHA5PqAnuC8sHm5\n/urf749u8+GIeY3v+ZlB6XyWM57tmkjhzHXIR6HPM2M+YHjspHk4a7KkZLYAhYrYdeDIMCdh2qGg\nns/fPznDWGYwWOAsYawqRhPoViaNAxvn6RVfoENN03YtXdagPdM8csYeoMowLS49UAo7k4Znp8UG\nWShwOCzWbx0Q6UbQZDqxfYRGXBqQrXf3IFIguTAnC1bNHZqvU+szYZHZkk9eXmL19T8VRe+Ci34A\nAGyX1raNYWyehkKQu+l7N+FGgZDSzLd9cf+iowCGitkxaSMg6ai2t9g4io/ftANGo7tOT21iKGp1\n1jMzoBCZOl1IzHk4W+ic0732VHe4nBIDczk/5lNbR1PiPl8b42cugmPSeElMxB0MXRIUjFG0Ixym\n2KbPRpwAQGCpHiQnD2mI0+bcG+WZx4VmQH7XI1IjbQ7jc9ocGY7UQowLel+indzSpEqbUp25nFAB\n18jHGavnhXREG0LrBZHbOqEJATGPpzb0peHy8gLLtvnmVjqlTNtvm3lbFUzHYNnDBDIFHkQgAlyw\naoa1WXs2jNgCthuzAufovDHtomkLjgHpPfh6uCeyziFfK+3wu9xuVYU4UY7E5brDq/tlivLLie+y\nnBXzDZf8Q33RU5k6IAOHta7bwAaCXslIP460Y5rIlHSX9xFeqHu7RIRRw1Dh8VZeisMnNJ8EgCsa\npMCVRCwxsRXm5J0UHb54732wsx94+APba++5BNJ7S4eOue1SCLR85nxcmOzanHYqGSi67MKrt4aL\ni44xGg6qeT6993H1Qy8BapISNtYy7CuTB4tvGLZFfAAWy4pmC95U0mpiKhAIE+sWyq+7VcI3UJCR\ntIwDzfkX8KwGOgUCfVStHNMfE5B05/6IGNPJqVMp9OZyVswHuCSDNZvMWI3YuE4JT7y4JMwcK/MR\nWY4ofTIJKVz68ypumG19B8tQHDqztMwanLi9zcKnhoaEh1Xt4E+VmKoQ9ZOH6hod20mN2sjQZKgc\no0ZY+f+297Yht19XvehvzP/aaUghSRMTiLY9pU1jDqE3RZMPRrTGeKgt8TS9p+xDjlADSmlJReor\nCH6yVcRqbCjkg/hCFanp4aRHkXtzDzW1olKamwZhV6m5WLEf0iR7k5peT+qz5hznwxi/Mcb8r7Wf\nnR41e218Zth5nmet/8t8GS+/8TLHlISNVSBIHUURBk0E6ilsnE/4qFpruOzUxteDNTHTqBmn3z7Z\ngwAAIABJREFU6DQpKV0cW0vonkJJwran02K0evKRxLzHGoC7IhBakczGfigXmaPwdZtObAKFrpC7\nY14manDhQug5bROi+aIAPE5Ic4jfF9F93nZwzMfGvW4xK1rKuJUWC0pjt2oJlAkGELYhp1lojyE0\nTxNx6OJ77eAL71eFMCCjin8eb8BKEjvzweyyrO+ZREmb0B5VSxqkVpqkvWRKW7xXfXdH2ETpLEqC\nLs/w/xokQy8i5u0szMK+bE6dmlzp/kuMuvQEFrJOJo6ULb9yeF8tjKBoKlYzWOYxVnaoGhPxN+fa\nnjszn+S6lr6FThLY6CWv01CDxR5ETcznXkW7h+UnqH3X0PNC7HdQzFeUug/Mv1DsLHzCN50+Swsa\nYUfxITWRODUPih3np542P2l1DVWjkblk5/upIrbjmoSdLXY9REA+kopZ9oBhh7mf8Tgg7vGeJLMX\nT+Ps4VxPdAx6YjIiCZ0+M/vRkgHcVgpizI6JPzNssegsQvPE515TZerjnn7Knv6lFgqfsHkXy0oM\n5cwQLSRaqffNHs1ZBQaE1SqwNeaALF7H6uKvKONLCHaSkNaFZ/aZrcFoqzJ/CQlWjDeAOuEklngW\nKAkVCp60mtdofYfDDWt7UojqdUmDBeYmJKyOkUkLln5OSQZASX0CYjMs56Vo/gpZ902oOLGFbezE\nWr28ZA7an6Rl2teEcfV5hkh3YdrwxPWh+XxQ0GAQNkyaTvwFBn99yxUFruZ3qYwro7EvuQbxMyBw\n/SxWqjBtXpA6laxo/1cdnlc7v7dq8n3toJhvagV6h5QGGW5XC6oZHcF0Sg8moSJmmAaR4FvWgxHA\n435AWyyh146K2mXy3eclQs7XEsYgCCsZnkRvKWdRAsOlezgwkESiyJ314mB4dCcWTRKTnd/O10ri\nORle6zgkGaswon3bsCLz6XctpBrQrdnpS1J0ScxNQTmcoMkml/xMfCqVn+cRTGaaJCdNCCgdnLKn\n46HuAO4j5TikVlUgunItPwoSa638fezEAzg05qsSOYZbiMoZL7MZfBFdWo+YMJPMpHtKZynf25pJ\npo4p4Ft2wSOWoYrwn5Z38n1su1i/UJIzXe6iT8LmvYSg7GhUZ0aOj88Np5Bm+T4Je2+eLw2xr5iQ\ngGSfai6sETbzFz27x3s6bZsSAWRX49dZmDP8PVAO25dj26NKV6Pvq2e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RLz3PsyYW+6wt\nxaMoZbtRQQoh3R1Ohr0d8w90BbawA0k71xXif6em4/nuFoIwb6jGYjojkykLQ2w9cybhsaCv0Y/P\nJ5SlMUwC0y6NtQthMY8rwxoKboCWZjRAhBTvV4V58C6pwzGrpPAscZQx129J/FA/8tkJRelkQJYS\nh2uECdIWye3Sc1l8gXufvGC0A9YaM7SAWHSplpDfGVUJQgeERUr6oRrbWdReaPU5Nbm68vmo5SwI\n2YYGLI3dGrDy8rRB1rFCjpGHq9A2jiBx0BMhZbXzEJ8RMYQzRlFsac2tREPLDGn5sbKZBROTR3/9\nf4T0fHZdy7o2lQHjJKShaMtMC+ET4LOqB9TniWuTJhDS5KkfgILnn1nD5aGHHsITTzyBq666Ch/+\n8IcBAJ/4xCfwqU99CldddRUA4N5778Wb3/xmAMAjjzyCxx57DMuy4L777sOtt956oVeUlqRJSGYD\nbzB/RxrxNi9zrIdNC9yyZ0nE7aaYnhNMwEe1E34InSLWJpSiJrkXFFuDhauF9WIoJfeNrf60BbKD\nYQaaWtC9B8YEBHZuRPNQggxAZDgjUhNmpo3RvEtlv5/9M55YBabjCwTstK+tVD3Kc0dnLuZMfKS2\nyDstzJ1hhWIDIn/uQs3c8V491mSk6vG05XBklMPgtznEKmxWAsOS2zmLXJ6M767FKKkzY3n+AZ1w\nQ+NsiDk8sb9dkPnuvPNOvO1tb8NHP/rR6fO7774bd9999/TZl7/8ZfzFX/wFHnjgAZw9exY///M/\njwcffPCCXp+5JXPUSYjy8XBJM2aMHsQXMIt+0JVk0wCwAHxfWrHdIvPDxXAkz5q6IYKFQKJiPOou\nBlZP1iQUuBNkqAsEye/iWl+wSKVSg5jSMmgejAFnfNqGrvV0xOwVZ0kyc0yeTVyR1s4UTD4QY2Ip\nea6EV4T+fE9MfoG8sUYoDDfRQGqRTqaKOaj35VvW//gSWwc/kixiec7GkyYqWtBRxlDzwoof9BLH\nuxXGS/SjMagqLGI8MZk51fuON6jtgsx3880349lnn935fA1fAODxxx/HHXfcgWVZcP311+OGG27A\nU089hTe+8Y0Xeo09ExqTlA5FDa0Ulawr1fp3AGKyc45rkH1eOIVBm516kWJnqdeSB/Z5YntRsVIR\nyoNJ8v3M64znwTVNgbH2aUKSJLhMCyeD9qEAul0vDc3fa4lUElCb0DM0G6nZJFgwoSacKCaMM4+P\n1bJJnAhd0PVtD3vI+laeHwIzg/sUWDkXfo2kxu8lfWswH1Yz8E6BNaJvMwNDKASbnyyscckkdIp2\nZ0+YxUP2HoN1ZCQcfAqBtpYBe1U7E14VvVNrVr+mxEGrF1B6AP4ZNt+jjz6Kz3zmM3jDG96Ad7/7\n3bjiiitw7tw53HTTTXHNNddcg3Pnzr3kZ04whmIQiLzHesj9fPglpt/rMlGDUrLTq0XtFnvFjAph\nUoxaQVyizzuvI+9T2E+N1CcEOPMcT9oKfF49JIXPqt45vkRtjOKaPKVqK0dWN0TFEe9H85M+CbXN\nOeVeXsxCQKTMs/MSCnxUmDDYbrf4+otfBwB0Cg3yl+T7RSi8ck1aE/ThBDrNW0kbA7W+Tt5PMiaD\n8YSYdnKwP0q0jAnBkKzxUtesaj7U7yNX08wNHpHD+THvtMacK9TSAD12S5QTBilp8AKA73+L+d76\n1rfiXe96F0QEH//4x/Gxj30M733ve/dqw28Mcq6cIj6Y1iQZoGgjoLCEptbML5KRZ2O6aNnCvoHp\nm0RysSG+EXDUPlM7CTakvq3A0Dy5gVqMMHLAoVcwn3/RUjNOTDgUqttwEAAmPEwCe3Dds2DEXhYM\nYOX9lmS66WSkVpiGUzpnpcAzTjrss+3RFv/z//+fRrBMZBCbJ4Y1zGFlqKEsvsNjJ3YYUTNZuu7X\nU46b1wyNXM8+EpqG1o3VzQTmaodJYYSgp3qfP8yYci4ExfExXFARFI+91j68tjIFGp1V2Zd/ts23\nr1155ZXx+1133YVf+qVfAgBce+21eO655+K7s2fP4lWvetXeZ5w5cwZnzpyJv0+fPo3X3/jG0CrB\nfD6AGrjNltox/raLy0eFUYOh89pAZ6R/2iNlzxkwSzFHcsGsCKLPz1578y15GqvYybJLEz+jr9iY\ncG0RJc41GKLWamFuZKI8CQ1IIAvxo8OQJxxltoychyDnnzm/GtroVf/uRrzhLW9z7ZXaDmLjoYBs\nfvJsnqCU77GNtHS0oCRW52d0gPWh2K61XqALLkLxygIli6fAZd2lhxv//S34fpyO9LBqFqTzB87A\n6wwXuyMQxTSfUqaWGTD23ocffjiec8stt+CWW24B8BKZb14U4Pnnn8fVV18NAPjsZz+L17zmNQCA\n2267DQ8++CDuvvtunDt3Dk8//TRuvPHGvc+snWD72//vb/Dp//F/B+ENrzxm3rIxuakJG1En2JkH\nY67iJWDAWd1m9s/FNGr3gPriM8wUqL61RONlsYI5rdk+NoExmoUXUgDyjHSo4i0C/MUf/VdslobL\nlobLNgtOLYv9PGVnqotqnNjamh2YQpId/v7t0Rbb7Rbboy16H+hHHdoHtkdbj14IlrZg0zZYWrNz\nz+mtFMItnoLUgujy4FHAYKMJOM417buuA2+6+z/h//2D33e4ZUF8aYJTp07h1CtOYVkWnDq1wWWv\neAU2m8UL0UoIkaPuY3GG2/aBf9p2HG27bykCtv75tnccdcWLR1tstx0qzZix99CeubWnMo3RAIPi\nZD7u0+T+0O+H4P/5758IgcSCWMwp5k57m6slWUsQ75yP2uaBqmRUmfr2H972Azh9+vReHrgg833k\nIx/BF77wBbzwwgt43/veh9OnT+PMmTP40pe+BBHBddddh/e85z0AgFe/+tX4ju/4DnzgAx/AZrPB\nj/zIj3zDsJMTWaEFd5n7BfXiGGhIY0UQmJmF6fKdcmGLZKSGgmrA0bwuJZuVCSQ0NMKsNlX6PtJZ\nYGMx90rXga52zjklLqW+qEIbsLg9synnCIozztIHRnMGVGAc9dw0jOGbcmFl7UMqGxMEbO8S42cM\nE/ANrJ0Jzp4KhgpHfTOtGLPL4mlowjPXl4gVSqnAT7dSDQnUXeqxhYiar+euemqgoAaH8gE/VUtY\nQn19WknaLvtRVPMkKE6AU1jVumarsYy1phAB0kTx+TRvst/DhG7J9+0zw2q7IPP92I/92M5nd955\n53mvf+c734l3vvOdF3rs3la9WrXbglThExb17yKo7l+bppvjR8p9ZxGXS/uqFe8cKaEGqaGwwK/S\nIsnFUBhM0jGiVkrYhkMxGj12zNwwT5ksDQ3MgjH7zdCkuCZNjWWEvWD0gb506KI4deoUxta0oJTz\n7jZ+bVsWz3IxG6xCQAVCEyIId9jG3tGx9Vw3wTCnzrLgFZdf7mlnguXUBm0RzwFtWDYLLrvsMmxO\nnbKd742IBRFzrUnSGV6Q4lyxf73kelbhRIGsIOKxMTCBfCgTmgljE6KH86lQDXwlQ1hOVAXKrEJT\njJ0mAxpd8M9qAsn043ztwDJcKAHpkZRMcvXWmoRGS8ZLKZVyLSVbMoxGygw1ZUpCDbvS+pEeRF0Q\nEzvGCLgIULq67dJo27UgErPXWK8FDqE8EN9SrAwAVrZFXdN5AF8WsO7MaMPyLTdu4yiM+Ugpails\nZNg48dY1XJSKl7RbOE5WODN4boV4GVY/deoUrnjlKwNuShPI4j8F2JzaYDm1TM6XhLGKtN3qTvVZ\n85Ex+bc/KOYwbP6wOZMJwmb19akGes3NDKGoI7nGoSfqFWQ8vq/Qpml11ozxVLWwP1E4FvmO87SD\nYj6ZYIGmEa1UWtVZQFyN8tlIBhSHn0Pc45jwE2DGuwRTKdLBEbYkpWhnTMi9XO54WFaLui6pHnvR\nlJtmm5XuGwNWAqW5g0TK9Qr1zA0GJFoTyEagbVg1tAFgWFhBCYXd27nIYjZkaM6lZF3ETE9SPIgJ\n6pkq3SCogVlsNgsuO3XKntNgThZ3HkkTtI05WjjxxBqmgTJXcrjW731M5SOMaTIThn1MiOplC+Fr\nKr6j4BjirqGcKdbrEJoZnjY/DjVJC5KJFjqGe4wTBXHO+Ft9Nt+tMQPnbwfFfAjCL1LY7TBbV2o4\nTCrevJgaRgUXyA0bn0jXfXy+mA0whS0KTo9XBBR2GwOFyVvakXUbC6/tQ9FEsYyB7gHy0RSd5Reg\nkGWJ4kvi5SVsl18rxz97QnfbGPP24TBaAy2jAQ2LO1xyl4K0lZ1TpfIsu2JXfdrXmcpGjdcWwbKx\n3Q7LpsV7ACrVDOlUSNlLmYhODeghhBl6MujOgLszaKQHuZBTqwFT46S7wLIwTFFfY4rPWfKCMGdw\nTwsZz+8LOnLVO917PMtlOzDmQzFO/IcyUXoVkSuGrf8Cll4gE9JbGo+fmMuICeWzGlpAkWb1DIRl\nWdzG8A15zTUMkhH5HLOjLMjcMBx2+oUDEBhEZexoDHPKgI4CgcHMYBSHe3DvnOdb0m5tHI8f9Clw\nEzekf41nFcmsOeGTQ6n3mOfFbTsQchL+CTWnPSPg4xhxfgS3FPXRTYOVMIL6PGVhJWrKdMzUEBEP\n0MmkZdNYdmZ8HUpCQ1uMFtoooantrGhjlLH4zyKEA60TYgZS4Pc+j/QwY+b387XDYj74grokydhX\nbqastThnryTis6EA8y390yDSiA1pMqPFzxKCcfGi4jKfRVgIhCPBDuAcsfWlImF64IYMdDS0MdCG\nwz2xxexe2IgEMSRjfwDKEdEJEyWSgY2pw/7TEjLgmRAoiQtRnDCmZbZt1RxTtvF1BHQXEWw2C9pm\ncVsVgCQhQtX2I9LLOKoWGx4wr+UDqbk0v2MSthqs5uda+sggN02SLOVAmrB5HWrJ7xCoZvndAAAg\nAElEQVR6ebP8QyWZXCMrmAXfvzh5KoVphTpt6jYOI8MiqCeSGC7IeofGfHQCgJauxiAj+F7sNOFE\nUfUXm06LNCdSjdf4zVESbqgTtC1sa4Le3X2PXGygQk9/tyDOMIigt5RsDoefIgNNFzQFfOOBE1QH\n4KGOxvSt1Miiig3VoGsYM08EC2AVqYOg7VnqL+huEyoyQ4OMnwhjBIwLISUKWSzuGVDTbTppznRl\nu5bGmGzeu2uz7VBsh+JoDIvheZyv+5yY3aeu5WZNF3Zggf2cfwBeK9Rwv6rB2kXsgBzCcaarWQJ6\nsdIIVUse7lDksc4x2xqI0grlFuHVdhmMWpBhnjmZfLcdFPNF0HQkjFknRvsRavzLpHdIfv88BSKS\nyqhBNO1AJPMGRFEkw01iUgO21Uz3dK54dwRY3HYPOKnA0OaSHn5SEQARHPUBEV9Uas2gaGpOxbIK\nkYRNI5b8K060MigE1H8ZTkANcW6hPz+EFuh+sK+XxQmzaL48wTW6WuwmHw/L5btHt3tWS+/GiPRm\n8vOw8zCHYbrSInbTAYiKarHDYFoZ70/VbCK58yUukvxXmcMRC8dBoQ7eQmoknBXzuIsfcEOBVjNu\nQAVyTDso5iumnP3JCslBjEW5gZNCyDRilmMxCoQEUiMBRYKOeSF5tbrIY0HczBLJlC2TzkzD9f63\n5rDX+mRVqTWERPf9eGaQCdpo6A57ZI9God3YlFXEcqIi31QBJUMPdeYL9ixOCUkBVTQBAA+gk9mc\niMlo4nPnzp+AniPnlnNG+627hzIKJw1Wzy4CK9LmLHnebMTuoiC36dj7bfyiZR86CV/JqClog1G9\nd1VQ8gFEQOs1reOpYaxKN8agIcL9EqI2JII7ph0W8zlh8GBBkk94G0MjpabYeUBoj8J4KdTtqsD0\nGp9zISp8jcXT+aSbfB0N+BLeEEA0s+4HqBlNitdMCIGXqCBTQiw1rGpU0AtpMJAzEYRD5uI4K0NW\nBiQxk9A0iYaTZnaPZLgkxpTeToVk9ngqURcUJZZXGI8ZK3Unec1X7d3Sx3ofjiByXJMw9oEVy2Mn\nPMHrwqkiBmkR9W+KhuLYQsiEVQ140WbSXvpzk7nIxcbQ+W5eeaF2WMwXImmNptNhQg8mHRtEUgkk\nEdQg/ms8awUVBYRxnDiDWgxxRC/EPWWjkH7RxGT2oe54kMrgTpTSMaTBDHK7SwAMDz9YOoigu/Zp\n7g7XuF/Rmtm84cHksPh/AaIWWSFI8e1E4ZTgKIoW5PiL4ZoPd8JEkyD84WMm83Ufb2i8ov0IQcfI\nsEJel1W2aZtqFSpcq+SQiTb2Od9i90GZnQKAQqCmppIoPEaBm7s+UnjWPtSZn5tr3RTp520HxXy6\nIgb7zBmPuwz2tMkOoBj2e5P7ZsazG/N++1NQSyHwbdyoqRhJxK4N0hErzoADMpJwLYY30DtLWXh/\nRDFGM0/oEAAW7O1DzWMBNdvQOyIeM6waj30O4RNwqhIAr2kB1ZCfIl6AGYg2fy5BvDT3PBcPZ2W8\n6tUMhuuKbZSHN+fLVK1aayJCtddySvlLrNFqrWLtHXrSLh9DbRNsOFfmNQ0GhK2FTnSX75voqPRl\n2jESkMfmXQP24th2YMynsZlSXG3VLHtKodBqVG2qQMT0igTkNZR+RYoSqlVGq5Wf1YvwpqTN+3jQ\nJkTygMUJHyGf6UTBYLJwHCIAPHu+W9kLKEzjeQE1EQGWFvv4mh/KIpI7MPxC+9EErNKWGn1mxHXz\nWeAKBGPxy/CU+n4pbohVlF3m4VjpsTNhS8g5yvcuQKnpMrVsrhpg81OGtlJbCnhMU4JOyIQ1WZzC\nfIVK518pmxWFNnIK4lrZcx8r6HlSR8YK+fJLSPOR2TIpVj3DfmR8r0ighoQldMvTc8m24w6uUgsl\nI2WMQoJVm6xhGRJrMrXL39/K84GMMcEXlEcKD7chRRZ0VStpNxoWsDQr3+m2bRN0WKl58YNOlnIW\ngUDCYUO3ZPMy72TC/TCoMp3/rW5T+6UUhKznwl5ZMH32aB71ge22u9YbEwRd72SIPM4aSLcFA138\nEbd0aBiEjTQNQrh4Gb/gzuI84UiTFgpjhMbSCXT58gZdVQakcDDtqkiB18IDbJftR2psB8V8GsxA\nCQzkEcIJB21iNOyb6u6dJFhUItNcKMf19t2Ia8IYh9Hv6KXGCpnMrwioI5IM78TPup4KZza/rQ1M\nqVvqC2eECPcKGJMsUKikbev7HiCdhX8Vo/kWsmA4uKeyBROG3VRgapkGJKun/Rfu9phjvsPHRE1H\nBnP7bds7ttueTpZ1uIG5na7tKiqgTUUGIeNF1Whq4LK2Wgg8s3dKyIfiJJZ2PzRZl9a0hIpyXLcL\n5YbCVZCojFeR2Pyc4xkPODDms0bIKSEBOdOEAxqawxlJG3T0kHh27Qq3eNA+zqcT5izkJtu6QNWO\nzIlM5gd8g6YdvDB7ysA+M9tG0VuzTb5N4q2dktOTO1NwDw+gCxRbqC7ARiAysIyGLoplKEYTLOQk\nyVxQkTb9TPh5HiAUiiCkV8y5ln8VKrJQLZnsiFCTwfQxigZMrceYaI2PzhrKbUDOR0D9TMSOXFJn\n2LpznoypZa3IgNxYW+1jv9g0pWti3/9hjDfoBPIXNMBCMdmvMIeKRr3kmE8Al+y5P8s2JkjOU5TQ\nk2A0OgIA5LlrqBMgnnYnhfEKkw/XHjTcK0FQw8H6tFuDOKWoodTqtkhpOVSB3qFqKquJnf8mXbGB\nAqLQAatOBtfYDUCcK+TQVc3BsR0DbQhkyd0eFVrtZcBClKvhhcaLz8s//k2tF/ZcMF11snQLqPfU\ngmEbaoGeo2hA728VVkLY7/Bx+HrWoraGUpqPwdPEeLCOMwbzXMO8gDvFQNsuDbyIrwIrM8eYzSfK\n35/0Sl8FHWQZGju+HRTzAXkqKG0yBtoJSzLelgRnY3WiK9xRpd6uJFplSrh90ccol5jLmknLop41\noVqYmxNNHIRJmhOEMmaHsAOto1nCbniB3PS4Nm2wYkmWBbM0O2GW87Qd5hQaklBMhfmgxf4sQmre\nhsO+05jKflbPsIJZKQVejoGjbcfRduufW6xu9LzO4nY5PwwxVFgpqN5kBHPF0WSthQZiGREyxVBF\nU/X7Wzrlqk0Xy5v3ckyWuDBr+aANMNwUk1oeueu+iido/fv4dlDMF2lkSrfzjKWZZFxtLDJADZzy\nGTERioA8671ZlMIGqzIdqbqavXcoCiQISOJaQcYdEoqk/ZULPFQtdLBI/O37Y9DdWaIDaNoBdQ3W\nO47cVhWxzJg2FE18e5N3IU5Xcumcc1h7ME36DDGdMAkJh9MmNRk9mduhONp2r7niYYbO3QslSTp+\nIgRcFmJ0Lc9ZozCkk2iih2Qe7nIH504V8D2IgQ4LVFQio6LhdgItgVgSQfGdyyJxWtNaoA3Sqarv\nwNI44OaS03yJu/mB/Wh0jMChaWsBAXi51HsKxFJoBsj9hNPY/q+z54yhhDDAAK9qbJ+T4fitIGNL\nFLhSvF/8PEYkAM+Lp6wYQJw3YX4Xg7fdc1ztWGeF9I5lLOja7MjmMSwoL15mXhY/kYgMOLzO5DTB\nqRdWxM2dBeHV1Nx311nc6GiLo26Fj7bbrTGgX7P1TBZ6MhOqFjgHClbYfkqkjZa8VzbkktlWCCPW\nKzSXjaU79CyYw2FsiQeOEScW8ft4d0nvyyVLb/LswKoM7Fxe4dZ+CzvaQTEfCWDKVoDZA7GtB8jy\n6IVgMpNOyrO0PCPhako+d6sjNVkuRvaBRKDMrmlut0WaG+2sxQPh6fkMGwG5LlBF77W36TQgNFUR\nT7NLZurDXPlLE/TYeZ3vs7lRg2rw/G1x4VAYzd4rbiBzGw83wJYDLNV3kCsZjxXHCDmpBTPAPhW9\npaYLrYUkcifimFcUpxchdIWZPnmxLFW4BtREvmCa3WTAiowyOG9V0qpHG5WaKuOtbWfheY8SdwRq\na7segtoOjvnmlpOhOl8zSWpFSHyiGDoHAF9oSrOwsRCLTZuLhXIluCRbxOz4e2gXeLUwO7shSl2I\nH1ZZBAoZm88evWfOpguEIUZ0y0KHgN0vAmA09N5xdGSSV5cFU92ZhYLDti9pK7azZNxrbfVFWUYy\n32AydNpuR0em5Ri3O+od27D3upe1961BZa9etYUNhsOZi8IAgUoCZuaqZ5I6+y4FPvvaV8dajIu5\nnYVmCAeT6cOHgsAxoR3h85ZHy+1tFAz5oNSCO/Q8t8NiPqSnCEDAhpB+IenWWRFFGhFyjpRszFfc\ncbBglsrx/IGokk3HytAeToyQyvG4svh0dBRtRA9cwGkp4V6lDehQyJlTAQw/ZShsYHGH0HYbAkLd\nIQFVbHSBSsNYGjZLeoyFwXpqlhWoYqEjMl/vW2e89Gz+03YbjBd2X9/6NepLYHVNeyk3XxliQh9I\n7ePiIaYwnT0uRH3OAPdTefdZytGYaKX995QIkQJPo2v0pPOxoV0ZO6aq9D5U7YwUBpmOh6S5S4r5\nXDtkQlc6MJhpT8dELN4waTpgOzKbslqXZdbbnCRWzx3oiKO3RrcSfNWeE/ESDx4boqUkgG+NSa3K\nWHofpoIbF3Vh3cAO1Qx0h+Huf1loSQFRLDzlyLWdoGGIBdRTu9jBi0Mt4M7XbPrApjVsNgu2fVhx\nIPEq1iJlXtkqRGfq18BWM0yw3RrDfX2buw6OtgPbreJo68VsnepiB3wsKCK9rzpZQ9BpqBggViU9\nrrkLYvjm2Qym95Fum6UtoGfSxknPqbGU2YB0gGUBXah6emAKQyImUWTCBIW8ALGx220+C0G0Ety3\n6+ejx/e3g2I+AFEBjHEYAKnBNJeo2mUKjcwRevo4WSFZi73ADbUBB12rBjwpEENjkd2g76u6ML64\n1fTeka6QSYrGt2WbkpX7iwGHndl7t5hgd2+vO3N6t2raDYrhAfyxLPavd6up2bZBKGT22YsrMWfU\nzH10bMc24ngsBbE9Ogqv5tZrsayhOHNjJ+WqmtXAiAz4diduFkjqo7sALhksyOfwnqzy5ghlreHg\nUNptdB5iI/7AecMahb7ThSOPMdTn2vo2vIZpFGQu68+/mI44Mesx7aCYL4xb/5uYnEFxgpPjdjjU\n+BEZqquGQyP/PztsBrF+MeLB/ixLpoXtOR0JoD2Rdoa/3thu8Zy/kaERjqsFU0zugSKL4efFD+jS\nAhmoQ+KlNQwRjKVhK1urXtasmrRtAPZ3RCW0nFsgbaY4S8Ercaf95+Xd/+kIWy8neORHZ4c30ncu\n2HRV28enzbZ9p+u/CMI6WdXLaIiiQDe1vBMZyfT0kNYpT72XglUiMWPBeVsIPH8lNaCIe67sX6OG\nC8Hvg+QOinhcqP3ztoNivtRjXCUOwgguAuA+8MZ9drQHyTyhfNZOCyn/fIFL0NwFF3iWOYAUBtSi\nNNaBeE424S0+DrKRw6XGEIa7yHmXS+ZZn2KiqqHqNUPFpDE1oUNT3XZ0gZ8bZx641uh1zcNL1uEF\nTic3+zKpenhmiq2B2390sGjmujIMUdewanmPCPm/yuwK3/MT4824HuOK6QCTpYW2ElE7E4Pv5Lrw\nGcq1MO3DjBYrqqXxzIDdpLyRzL921jjBWOLZcKhN+MmylSCsnZO6z9cOivnYiFoYb6vnj1dEw2a2\nHwAwbWgFWUMUqzPTLJVyfvMzMlmjg4XkU54nQBCT3eMMyVqZviD+FqD0TfiseCUJc7g9mtCL+9Ya\nMubo5odXnh6GYHl/M4ZvUffEbNgqJ8gIxciNyRi+WyGcHmNguz2ybBZOpbTwhio1RPTLtawmKLMd\n/dZJOrkw6i508kw1BzxDpjXP3FJ7jjNJ4zl95QEDJfFaEPSwLMUGC+eM/WueotZ70ZZlTnIUFPAE\n0Z5/XNYkTj+qUOY87aCYr2Jl6oaQSmAaUq93IBlirD4PFkbGtAASOX9Se5jtX5gUCYMtlauHc4f7\nyKJ+i/iuAz87PrxurTCgd6meGdAk9p27sNHstUPhKD0/hjNbSyZHi+1JvAcKP7sdGFvTvjbGOj9l\nloLAEBpdtbu9ac6pbe/4+tGR38TaNebpHIVYJZ43MzYLVxkjFw3jBFrXfbg9SZsymFTm+ips1GCV\niU0AAlDmedLJInFW4Yw08lmsfxNzXKGk/z+1Zg5S4eiKBoMI9AJOl4NiPmCGdoEDMSkI+5vwzydX\nkRkwRu+8L3de23cVGvrHk/g04mA5iNS4ZvuNvi3waaQETbwSzyEh1swH9qKheN1W+aQ0+Jsz3qSx\nlHaOdXppDR2E1J4t41B9djLNMHlNyFKIiJk8295jflKUcWtQ2eIlDqtF4LXXwwkifjgn1JwWIU4n\n+OvP0GRaiTM57Fp11KJreijTFnNEgQsXqgEP6zXzTwrxNEmS+eJfebOq2tkVYKgDsyABrKjVMe2g\nmM9BErgCTSzozMXP8xU8PFAGSvf+2hGTxFYZxCGQlgC4S1k+c7PZ8NIAmSEQoOHZa4RXheBH5560\nAmWVhO2n2sZJsYhzz+HEt1kYDLZbmVqXXjl79oBi27do2vw0WsK9nE9wTEAIoH0TL64VeB+hY5xP\nrhlzo3MKzjCUUnEoiUjURIE/Y5YvkgK2QLzsL6Z1lFwEvpaUUnCRCYCcIf7rUNhOEDLKvlnQeD4Z\nl+cmLnnBysaXcmPNxomiS5vj2eugmC/mCz7Bhbmo7icbgQsXHxDjIwh5PrHULqoOgnIrhru6QwC4\n5hq+mdf4dw5JKHxL6hgYQrvG7LRt32JpbSKT0CACg0AIRyqIy8YYxoCSwmMnkZyQUBWtZenBheUN\nAbBuC4qASLQgk2Aw4DA8pU2yEO/Ie1jKXWEMBVmMIHk+ofdzKCIkEHafq7G2LDZf6gkDJGhFhiq8\nS9KssPHoI8s2gjSQGpR/c9sY74cIlmUTSIXDHTQfmGFP+VGWnebDsvYSIxUDa7KS4QJEOYTuvZpI\nu+2wmA8p1QBKOQHCvV8kT6HMiAOGbeUwiFKbeYt+K1OyCC34DLJHhB56ajPwORHP0nIakCSEZM+D\nYMcEWcLO5ztX0kNasQNdW1b820Q8+TolkWnB4Tmv1as7a5Wwzeq8+fPVo8SW5B2kvYJn+WmMiNoh\nhINDYh4MM1KTsP9kGHUE0itEDducc1WBYS59oIo6l1FR2qRJlJOvCMTfE/ONLAzVAGCUdeU1ksKF\n6Cd7SU8tCvmtQfH+dnDMF97FiOWl5IXOwXcOOCS7S+nm5RFYUcuvBgAot564hyuklX0ZkxoZLEgJ\nRgLOViSimidONDWiDgUWSULFTPhhF6ESLgqzFk3vv3T6/11IEWQBLPugYBaGlCTv6G21m2ijeIqO\n0C7qFEaEWhJj4o12MIkkXHWBl/f57405nnwOtY05qpqnz40RqxDzw7kBv4kkeonxt7aEVj1fI+KJ\nfNIiPHgBU9KWlpu4RRBwfnEPMntZi//WI8d8IkGv93Ht4JgvIU5WI8uzBBK+VDEoSGZVGOSDVgYS\nN+A78UgsIDP47dkp26N2pwi2220k5bqQhLnyS1ZK6RJXiM6E8Lf4O6ZMFv4tAmmLn/POcEQ+NTWQ\nJu+x0QYqsHzSgMGAaWetibGFzZeJB9MrynNas4Mw624JqnSRlsdli2+FQg+bvPbZIK7Vpsn4m8NY\nJiRIaqpMstZgeN6DMEu0zInTi03yvFZFAEmxpd2Qc80tsWiTMwZwO72gohzBNGfHtcNiPrV0qiAJ\netSCosiM9idzOKnu+RmUjpSEsVkBjY9Kic+/+Q5DnD1tg3g34swAoGqyIrU1F8Ik5AjmZ/9qqhfj\nfYufZS5q9s3igbuCUqGjx8lNo2duY0hbyHQaTzCd1lgpn5sQ2EqwZ9aMKuKtGesze00AbDanqKJR\nIVj2dKVVWPaff8eA8u7Qcw5XIZk/C6in9Pp4Knqou1RCeBK6D74ERFCA74DhO51Iaj8EfhATyt7N\nYQeTiudypmC09WW+91jlih7XDov5gNwKUpwDKAxY5t5vSOC1LqxT3fLMPYznuURNJgDQBGPr6Vyo\nDOnvEpi24GLGxzIxocZPdp3MgLwj4GWewd68T8xO8S453DZSGkgGybzT8iwB1iW5wiYO+qczx37y\nJCNoRkvp3KH03mw2dCmbfeuabbj9HHB9UpipFWuyQr1+ApucE21WnbvstjCNV7f35PzzTQEnuRZF\nM5ngoMZPBqmeVACl/GOZu66OnOzZuY1L/ahFZtiIMWdbwfTztINivrqRtoYR0vCB424uhk7XZewv\nLo4dCCTAylBxIAiQ76QE1Pl9EWaQebH8xflCAZjiHrDDtS+fEwxfbIrmfeeZAHEiEiiZ3ZVftIv6\nYZjmEm/xPIrvdBKgwPiifb0Pym1LTCnzdzLO2FrD5tQG4ufXmUYybS4jPYa00Y0pOZeSDo7V3EWR\nrNb8qxElIiicPGd8WqeyYDafbn9yzqnRtOSB8nx6ICE556wuY0UapAlj2JIKVzRsCAyxHQ6j/n1p\n2nxubwWfcKuI7+MbmQkShEDGK/v4eBjjxMSSkzvcs6UOd8OeEodvRYwnBPK7WcHZmThsT0Uwo2ki\nN9Q1IWzSt9tRRXMygwXxGGc3EuqSWRvqQdBlYe5m3RIl4Z5X1WBufpcCQFJIueOBcoZezLYsWJZN\nMOOybMwuBNCW3FI1xoCXG/U1a4FiIFJs69mmzBBIHp4CapNg6IS5CO3G9ENuG0ptw90YnOyxQlQS\nFDQ78AjH1wxoWpUoi/NYEA01bEE1uu9wndIOivlqTC/HOabvdWhK5yAUDYbkdX10jDAO0zslwETg\nLHNePWYGF0c8yxY/bYDWZqYh4XBB0+hvYSNW+6ESvgjfmAtn1zgkZJhFMkHa4LP40WNWcr46cUSw\nE3vjMWWoBEbmAywboxsTUktwF3fzXRIUXCqCpW2AnnsjrbMSxX5ZUmDtdBABRjh6DCR0LYH7cGBg\numbWoNQ4XilAi7fUNb+vVIybienpIBnQ0aLcX1gNsfGYjM4dKNMOMH8GEZg7lySdMm2h/Xf+dlDM\nVwmRAw4ZtdJqQNpTlG5hX/ljWmxjKbU+80UWVMds2CuqEEBoDUg9GYv2Qtxmxr8vXKyjWhwp8jmd\niMrDw+6ybkl2zx9iBEYGbPFT3ClhjkVBBtT9WmabFKAa88xRlDkZw8IvTTQ24Yoze2uWmEzeqJ7J\n1AV+PcTs0jHiTc1tQ9ftEDHXPKjtijPGpoWVoguUXNOx/53FlXKO1RkiEUjOHddUPCkbcX+ZHeXm\nXfuaqILe11QOEgLJptrPcIzdJJeQ5gMkFoqYM+HkmLZ81MwLTlJITRrUAcUEmfrJsEVqLE4+mZfn\nkRO2Mb2LEHLSGCUDXx1eLWVzrfGbQ9iJqZHEEdpoZgjAg+pBW4S3sxChoKoiof6uQPQ9e1VsPn8I\ng8mNyccBS2Uqvc/+EzYH/AYgDVgmQvfxeNFZxvRMo+kMP0P4VQC9R3tyXgmjIatrSlAc87zzeXWX\nRwIu9bEoFTkA04WLn9Sbc0Ds43PXCm1wDVb9XreDYj5KfOHxwoR+I0MHbDynDyjCqww2g7T2eSvQ\nE8WxksayTv8yq4GaNSU7nz385bTNSDL5d3F0eCebLvan0CfqCcQt+5kbX9UREMMCrvkawJ2pwhNl\ng4ucSYBgjMokMRZQY8Kh9JLagAwX2iHnPchdJFBJjJ5z3poVhRpzEWIKtsmpNn1XKllP/FKEhtqe\nOjvIs6G5oOSch7ZnkN9jjhoDqUxTUE5hQOHnBaZHnR0ISq8Lumgz2lgbjnvaBZnv7Nmz+OhHP4rn\nn38erTXcddddePvb346vfe1r+LVf+zU8++yzuP766/GBD3wAV1xxBQDgN3/zN/Hkk0/iFa94Be6/\n/3687nWvu9BrOO5U7RpoKxgvDWUEvJDg2B3wEEQIpHbgtbslvZ3ggMJ4fJVOicsz/NVCxDIROqVz\nZr4ghIoMtwJbQ6RThC1TiAgZUM4tShRUBVYByGzmHFONjRWZANp98QxKfUhs5ZkybULopNYVltgH\noXYG9lW48TTzHwGkEytQCUKb1KB1IIsyx9SWAt883nybGWO9OtCWTWpsAFFzpUluRxN6zMlkWqZt\nxTEKjGa1gdTfyXNExPM/qxZOxmu7z1q1CzLfsiz4oR/6Ibzuda/Diy++iJ/5mZ/Brbfeisceewxv\netOb8I53vAOf/OQn8cgjj+AHf/AH8fnPfx5f+cpX8OCDD+Jv/uZv8Ou//uv40Ic+dKHX+Dh1kop5\nJh+SeHKMCTdEMiuj2IRsrH8ZTCG68nq7kYyGoT0mkAsTB3HsebZ9XrRIcnlZlOJV5NgA9wzS7lsx\nTcyJxBimawqkppBl0B7AVCkt2LMwNlm70kcSfhU8CGJWRwLGxE5c/vKm4gTq9haIIlLDBdQOASvO\nCMgygy68aknEELAAGJczW92qSQtm5BKzH6jEXstDcma/QcmYOoYBVUvXBbCDNHwOVL1MiKYDR3VV\nmn+3XbDE0tVXXx2a6/LLL8e3fMu34OzZs3j88cfxlre8BQDwPd/zPXj88ccBAJ/73Ofi8ze+8Y34\nx3/8Rzz//PMXek0ZJCVgfGSDSfke0Idok4xK9/LQGUJGfRbi9QI7URY4XP+EcNGJhESTM8b7wRZ7\n15zLyGSVgIHsByU5D7+s8beEfvl8VsqylPsW77OfizNNQqxdWVQZUTwYXNLEin0n61II/t107p/Q\nA7tMtg5zaqMfHLMzV8JArPqYz+Dakjk1FyoVFWEl2T3+Rnl4/p6OrzV9jMiA4ssJCtboUSrE0mLr\n13X1NZ0que1p35DN98wzz+Dv/u7vcNNNN+GrX/0qrr76agDGoF/96lcBAOfOncO1114b91xzzTU4\nd+5cXHtsc8nClaFEA3LyYyE1F5XMkbYfiFxjotFSOqYNFkoqWlsWtJhEu7+elBt9nOyGXCgiM5PM\nThTcQ0PNWxlCPU6F1J47OyQKU9C2NQmewW4gIV04BMo2nPo7coqdIZCTVt9cBMniQWoAABN4SURB\nVE2GRQTC8yGKwOhbT+cD+0WlK66B0tZKgbnij/JdaBSnBDpBbI7qHZLzPvVZ3VxocWkaIXVuAdEc\nW2i2gj0oFKgAiNBY0hCQKHOB+v6xft/cXjLzvfjii/jVX/1V3Hfffbj88stf6m0ACBte0pXTX0XA\n+HPs97VmC6ZC3jCmhyhGt8WIUhB8n1ODuE0HeByxU4KN7L+kVgEQp7XyE6MZjTVIieiMT40a2gDl\nuYTXACqTwO0+Mlzc4hoLGmXphwzA8zSrFm2S7u+gdqlIS+KZJNrpiFUnOu71i/xWQulpMEaqsaFU\nzEvI6m1kqBSg5R1FqMZrV/RbYb2FQrIrWRu13uTcFMyXHvKw7YPhEMKNgttelE442opBBSGQZxyk\nZXbP114S8/Xe8Su/8iv47u/+btx+++0ATNs9//zz8fOqq64CYJru7Nmzce/Zs2fxqle9aueZZ86c\nwZkzZ+Lv06dP443f+u/x9nf8J+t8iMQV1CtaiQHwZIh5sHtZuVw2kxeXTSPXkT+NHyoEnLfqAKm1\n+Nybbvk/IP+5SH2ZQZWIhSS4M4LPW1qmkSlmQRGwFWkD1jFGBr/O3k7aOgUOgBpjZ4aKtuHs3PD6\nb02G0ySsSuIz3EdhIs3Pff3ioEyu5+AZEfn+RC3wNcj5JT001zrijiY6impmD5mJ7VtveRPe8Z9z\nzcLE4PRUSFJbNQnywxWRUYjJJCQefvjhuOKWW27BLbfcAuAlMt9DDz2EV7/61Xj7298en337t387\nPv3pT+Oee+7Bpz/9adx2220AgNtuuw2PPvoo7rjjDnzxi1/EK1/5yr2Qs3aC7am//iv8X3/w30AY\nSdWdduBwzZfFXOuikkliKrhwhF4BHX1hHBZk0VvTdNttd4JJPL94vQ443GMWSHhBy45nAfADIvij\nT/xeMJ/VbKHTyFLCXnHqFC47dcqfbe/YeEB7ac0lOgmM2s6IrMW7W4zL0rty97SNuaEtbSbIlfAI\n24dQyRk25gmCJz71R0DMUipQzg/LyBNO8jw/nr+uw4+Q7sMK83p2zBjw8yB6YUgEbB8r5JGQwU8n\nbjnn297RtwObU6ewbJbZ7grY/F/wBw//3owMXPhBEVWuQVnD90mm6wUjNm51ql53t4OXBdIavv8/\n/p84ffr0Dv0DL4H5/vqv/xp/+qd/ite+9rX46Z/+aYgI7r33Xtxzzz144IEH8Nhjj+Gbvumb8OM/\n/uMAgG/7tm/D5z//efzoj/4oLr/8crzvfe+70CuiKdRqUzqzzEgyzzqo0LNcAPq5pNwTEswXLjL5\nFVGCnqlhPJkHKXuDYHNHusW+4gw8b6JMD5OAdBlDMshZu7tCW2F3qWvyASRcdAlLhqFNaO8wuy+8\noV4prIYr6r9qz4iUnQb+wPgpZN5k0Bysj7F4VCsx1wQIAMF4UaB4aDDpDEHz+Raa4fHfLb4PASfM\nIPH30KZsXNzckUEveJQP9PeJb5GaQD7nR7hE2S87R3G4Q6qZbyC6nEyJ0sfj2gWZ7+abb8bv//7v\n7/3u537u5/Z+/sM//MMXeuzeFgtA6MQFdNuANgJrqkwEAQdCQUDJPJN73e26SoQ6BjoEGFmUidcS\ntcS7nAlY8Ii1k2XYgi1LC9g13Jaoa6BIJg0p7wiPTFAJ0kshx3ajaa78HcaL6RDguENSFy1HOAuk\ne9+GVowezUTkGdiuhAq/ci0Q515MZoILy1E80gWaslhvp/NIUkDUd9fwiEFLg+19ZEl8IHdy8BCV\nWC8e7+YoJda45c76eUwIZJQxw+KwY1lDkUjHQzAgZf0Kuq7aQWW4zNLSyZCLNLhwA3MSrULXYyTU\nk7KYsXhrjYmYUH5XyBBAwfCYiZfwDZCAqSJlsYJ2HK4KI0PeEy3PR0K5oQOi61N2koll9WiDjPWc\n8YoaZmlM+4nwPRmJqd+Zr2kakMyb2ifnJmEstdJw+GpEaOcqDmisIWuBggIn1tbmry1L5NLSw6h1\nHABEczcKYEJm9JIBVYRZs0ProSMFMJFD3VKUa5wP0SUFDR0unLOqgZEzNj/rksrtVDPcc/uL+mc8\nD2Dk4OmUmPBpajfaY76OxZng1/piM74jPNLJzgeL/pQpDUkKRaYSuYQj09bd7+EQqZAkYLD9r0It\n72LC2vX0lLhRjBMIm9ggWF5jfRmrg1zYH0S+5kw7WfhXNa8FcqNt7Lmb5nUWnjZe9snuiTPaCXcr\nyoCJshwLvI8SwmIKm0CLrZUFjIkX29JijlsTFyIJr60Mhqz6i4C8EImzFrlhGCJYdAn6yzVN2Lmz\naMe0w2I+IGAjbbGAV8MlKuCLp4WrjO1amYgpKwVJJKR/hhHgTE7njAJREg5Yx9yQUGXd6T0tFqQy\n0rQzYQXplD8VdJuretkDEqq4Leg3VClMYZKgQLEsS9pLk22TuZlMxLCx1Wx8wm4XHgFR+ZRiD5Wa\nN+a8GD5WrxxX9ugByJLxNhl+n8Zz6QAhkzt4cEeTaVQeKjq/l9qo2Mq0RTWRFKFEFSB2oA6IGRO2\nRvUCppUVIc/OoWrDnM/j2sExXwQvdaQ0t29Ms9ivM6ID/IRjnReraBGoxi7vKqEZ+1Iulr+tSeZ/\n5sKNINiQeg576QmNZ/rf9YASEkiYKJL2UPPE6ggwO4Rbokecn1zcbNnvICw013w2hyb9fUIKWVTN\nxQ9UU6KTeFtr0L66zn8JYenj5EU7MVifj+4QUaOqmWu4YS8sBoILzEweb8JzEzXmS937u2BB961L\nMcdmIQIA6lEDqnCnTkmedxu0LV6TJYsFGcPHrhqJsvMhnGIlJBXBpQQ7qe04EQKE50xcasmaEnW1\nUEB4vCqk4UJoMCkA9a0+KBAl7ERK+mKH+jtUvXSe0nGAWYoS7jrGCV4j4zizZlWugTEyyN+EnjQt\n/aYA8b9dNS9e2sHmgH3IMXBeE14K1hqXJR+09I+HkEALRFytFyu6HW23se1JYXU4h+8+IHHW8xoq\ng7FfzU4aBL9Fkyk3kiGVtmI4Pr+5TRoZLb4GY2Tfx8gxqCOpFoV2NaH2UC9qnEgBkHCMpWMrhZ54\nsJ400keH9PWMze2gmA8goZSfcI1DLcRrKL1rqhEhBhLfU7LzmqkimQjUYdHSFmMOD3rXfNDoWxhH\nlSGr9kgbc2+jQqzjJdHH7wI014ai6Nst4C5xutRZR7SJoC0ufNQ8rs0PxyTBcOwZKpF4ziRYvA+7\nUNgEYpxa5POGcq2dBUhPNA+4dKfXsmDh3Hjszw528ZDISGShWNluOxNZ4H6BkfSk1omWmPB5G1Mu\nRCxAkc45Fwq1wkliMNzKjCA9m04DzXel5PPtaOzWmu3qP6YdFPNxMhEaJh0I3JlAbVZjOyl93F6R\nnP4J9oDPq1LXmWzYmeu0kQhFapzPbnEmH+lFJO6v9mQOCqjlBv0j/+cS1cccDgcn3qyc3SNGxTqj\nPL2IwkQdVqsI1DUSz+cLGAhkX23Cog/N52xKFkgpdf41q2NSt8kU0J6l91trWNR2IZDLhe8j4miI\n9Q3d4mUSJTQ5QjvzM54nyHWqwiPpKddk1mUxIT5es1G5Grxvyo/ORyIEvmvFmorIygDHtYNiPmL1\n5JXCjPweOfgCOAHMcIoeyAicF21pREEiT4KsZ7jpNMlpi/FaK4vQkFt9/NnlH6DpFVXNHSdkYoew\n7NMQiwGSKcZQbLVPlc3Cg+ovs/P67M8Iygc0KkQ3OXiM0U14lSPHAqoVryoZpYlDUCIC1g6FZ7LU\nVbT3hY3lk9zcVhNNB1Rdz+YcyKrUaXemdofPQQAfkUiMjpqmgYGTGZu00ICjj7if5espuFVr+cki\nRN3JRSraCd+IhJc8Qjn/krsaXpZmlnhwYJVU8XelPlRiTDgFZ7wJxkqiVEpq3sOyCfaYLFrE6mht\nYVnyqvGkvjZhr2sb7nQHEjANKLd8mebSAoOpiIvDg+50mrdzlNGJN6iXdF6leRJWFRBEDwad8vtA\nFP6MekhmWQGwaFVnNkswA7c1jewchUpr0DYw3BbaLLarf8Ta+EtWuwFSyxHaJvELYBqmdwT0QKKg\n2Neobh9zDobaGLh2Dv8n251rMs2zxjrXuQQhaLnuktJ8AcYc9uWxX0VCNlJ1vSfxyFQYFwkfqFI5\nf9z9LMiYj13GlKvSAsaWilyT4yJL100mU0WfThhrqMNFr8QuvQPLEs/jyalwYcBg9rJkBgtAOq+g\nKHf+M0yRmTXlOtUJvlaJb3ORz99ZLU17y1LvON82ZyNyyGaGiUUNYZZz4JZ4fBfjR5sEI+FeAzBK\n9k9m9qTXWqFRjHgfRA1cReEzVQ1IzBXCLUwe14DKOW6gA/UCO4oOi/lq0JXQreZPVq4QShaRaRdC\naD67oTgUNGAIbZFwjEGKoY/ogy1CwhCmJSHp3R9gCxAFa43yUrMxoZrM5M8NxQvyY25WJRultKbH\nc4Y8QbxFQtdt+lFC0JkhkhMmJpNISsiSHQ7dKRBjdnxcY+XI8JdHvI862u123sP+i1jZxojJCscr\ngIzM8ZUWTG/9X4rsSoEqkrm/TLiefQgpiDN2O5sqNHnS7pSi0YvwmeBwzvMk2GIxzt8OivkApHZC\nRZcllkSKBYIaquSuAHX2kOZnxUqcbIs4aVZ2egDANdAeccbnJChcq85Z2XGneP5tsLb23u4pELcM\nJBl1KbDcV1vE3fbUHM0ZMHUiNSArjcZIKWxoGw3fkRDMmP2iM0gDFbidqpVNGaRv/jyzT6X0w+bc\nPIhZwyWFHjWRZT61sGt1dBdaDdvI79RpvlJsUCsRco6JtsKOjq5X3W9XcryBjFQBdENNIzW0ZfL4\nqVa+pudrB8V8IYVos5Xv1gFbnkJaS4IDEsxR4L9LUHqgLCEXsEVoi28g1RHMZxIsoa8qMhXLRLfd\nTwJHuvJD5AliUVUtgrW0FodXso8iHjOEu+zjOupK6yedQbKY5Df3fvEKhlguqVAeNmEpQNqv7H+L\niXICi4rSGlq7zj2vJY3W2B8hK5xZ+7Qu1pa2GKyWhsV395OQedgnk64D6vO5pAPSh19roY0s/87Y\nH8cvQIQ3oh8+3+lkYtHhggQCXSSKQVTQ07g/+uVMl3OGf7md7C9HiwFT+vhiro+rAhAScQe7S9pd\nGXAFMOZE2nyMY3wuQmiIMrFFes43+7fCMu8yxxHzkiRYf/TsyasLuTvWyQbjPaqxr4+acH37BIHU\nmXR6S0ZEA0yQsFeOFqKoRCC1T4mx4nXlmrhSAqHHlhxCxbF6155JsMJXaYiBQnOMgc3mlNvHmZMq\nrslFs391LNj5hM+kKVCgPeB2e17O69bjDKG0j25LE92hqJN20k7ay9EuWL3s5Wx1u/2l3k7Gcpjt\nkMZyUMx30k7av6V2wnwn7aRdpHZQzLcuqHQpt5OxHGY7pLGcOFxO2km7SO2gNN9JO2n/ltoJ8520\nk3aR2kEE2Z988kn89m//NlQVd955J+65556L3aVvqN1///244oorIlvlF3/xF489Qu3Q2kMPPYQn\nnngCV111FT784Q8DwL/KEXAvR9s3lk984hP41Kc+FVXV7733Xrz5zW8GADzyyCN47LHHsCwL7rvv\nPtx6660vX2f1Irfeu77//e/XZ555Ro+OjvQnf/In9ctf/vLF7tY31O6//3594YUXps9+53d+Rz/5\nyU+qquojjzyiv/u7v3sxuvaS2l/91V/p3/7t3+pP/MRPxGfn6/8TTzyhv/ALv6Cqql/84hf1Z3/2\nZ1/+Dh/T9o3l4Ycf1j/8wz/cufbv//7v9ad+6qd0u93qV77yFX3/+9+vY4yXra8XHXY+9dRTuOGG\nG3Dddddhs9ngO7/zO/G5z33uYnfrG2q6SrcCsHOE2iGP6eabb8YrX/nK6bN/lSPgXoa2byzAntRA\n2BjvuOMOLMuC66+/HjfccAOeeuqpl6ObAA4Adu47UuzlnIB/iSYi+NCHPgQRwfd93/fhrrvu2jlC\n7R/+4R8uci+/sfavcgTcRWyPPvooPvOZz+ANb3gD3v3ud+OKK67AuXPncNNNN8U1HMvL1S468+1r\n6yI+h94++MEPBoN98IMfxDd/8zdf7C69rO3Q1+utb30r3vWud0FE8PGPfxwf+9jH8N73vnevNnw5\nx3LRYec111yD5557Lv4+d+7c3iPFDrlR6l955ZW4/fbb8dRTT8XRaQCmI9QulXa+/r/UI+AOqV15\n5ZXBVHfddVcgq2uvvXaivZd7LBed+W688UY8/fTTePbZZ7HdbvFnf/ZncdzYpdC+/vWv48UXXwRg\nB4j+5V/+JV772tfGEWoApiPUDrWt7dbz9f+2227Dn/zJnwDAsUfAXcy2Hku1ST/72c/iNa95DQAb\ny5//+Z9ju93imWeewdNPP40bb7zxZevnQWS4PPnkk/it3/otqCq+93u/95IKNTzzzDP45V/+ZYhY\nbcfv+q7vwj333IOvfe1reOCBB/Dcc8/FEWr7HAGH0D7ykY/gC1/4Al544QVcddVVOH36NG6//fbz\n9v83fuM38OSTT8YRcK9//esv8giy7RvLmTNn8KUvfQkiguuuuw7vec97QmA88sgj+OM//mNsNpuX\nPdRwEMx30k7av8V20WHnSTtp/1bbCfOdtJN2kdoJ8520k3aR2gnznbSTdpHaCfOdtJN2kdoJ8520\nk3aR2gnznbSTdpHaCfOdtJN2kdr/AsmsbOP7PbhPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113053320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_img = np.mean(data, axis=0)\n",
    "plt.imshow(mean_img.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 218, 178, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first step towards building our robot overlords.  We've reduced down our entire dataset to a single representation which describes what most of our dataset looks like.  There is one other very useful statistic which we can look at very easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x119f3e048>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FlblZp4sOxax0vVq5J/SrJwW0l6aUYkZ8mpfrf4u8tsVetZwAzScMXDAPYeI6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113158320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std_img = np.std(data, axis=0)\n",
    "plt.imshow(std_img.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 84.87342576,  83.21022533,  87.81833522])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_img[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218, 178, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "So this is incredibly cool.  We've just shown where changes are likely to be in our dataset of images.  Or put another way, we're showing where and how much variance there is in our previous mean image representation.\n",
    "\n",
    "We're looking at this per color channel.  So we'll see variance for each color channel represented separately, and then combined as a color image.  We can try to look at the average variance over all color channels by taking their mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1198c8438>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wDrQm59ZXqmnmOmbF8mMQoWB9Ia0bdaGaUJkxHzOdCaNIx2uQY4fPfFcOMaYr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9A0mzpra6FoKQrbOrvODDB1HYlrqeuc9pDDIY11JhXUpealTJvc5yF9QRhDLkCvsXcxgsskxr9q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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11314c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.mean(std_img, axis=2).astype(np.uint8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is showing us on average, how every color channel will vary as a heatmap.  The more red, the more likely that our mean image is not the best representation.  The more blue, the less likely that our mean image is far off from any other possible image.\n",
    "\n",
    "<a name=\"dataset-preprocessing\"></a>\n",
    "## Dataset Preprocessing\n",
    "\n",
    "Think back to when I described what we're trying to accomplish when we build a model for machine learning?  We're trying to build a model that understands invariances.  We need our model to be able to express *all* of the things that can possibly change in our data.  Well, this is the first step in understanding what can change.  If we are looking to use deep learning to learn something complex about our data, it will often start by modeling both the mean and standard deviation of our dataset.  We can help speed things up by \"preprocessing\" our dataset by removing the mean and standard deviation.  What does this mean?  Subtracting the mean, and dividing by the standard deviation.  Another word for that is \"normalization\".\n",
    "\n",
    "<a name=\"histograms\"></a>\n",
    "## Histograms\n",
    "\n",
    "Let's have a look at our dataset another way to see why this might be a useful thing to do.  We're first going to convert our `batch` x `height` x `width` x `channels` array into a 1 dimensional array.  Instead of having 4 dimensions, we'll now just have 1 dimension of every pixel value stretched out in a long vector, or 1 dimensional array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[253 231 194]\n",
      "   [253 231 194]\n",
      "   [253 231 194]\n",
      "   ..., \n",
      "   [247 226 225]\n",
      "   [254 238 222]\n",
      "   [254 238 222]]\n",
      "\n",
      "  [[253 231 194]\n",
      "   [253 231 194]\n",
      "   [253 231 194]\n",
      "   ..., \n",
      "   [249 228 225]\n",
      "   [254 238 222]\n",
      "   [254 238 222]]\n",
      "\n",
      "  [[253 231 194]\n",
      "   [253 231 194]\n",
      "   [253 231 194]\n",
      "   ..., \n",
      "   [250 231 227]\n",
      "   [255 239 223]\n",
      "   [255 239 223]]\n",
      "\n",
      "  ..., \n",
      "  [[140  74  26]\n",
      "   [116  48   1]\n",
      "   [146  78  33]\n",
      "   ..., \n",
      "   [122  55  28]\n",
      "   [122  56  30]\n",
      "   [122  56  30]]\n",
      "\n",
      "  [[130  62  15]\n",
      "   [138  70  23]\n",
      "   [166  98  53]\n",
      "   ..., \n",
      "   [118  49  20]\n",
      "   [118  51  24]\n",
      "   [118  51  24]]\n",
      "\n",
      "  [[168 100  53]\n",
      "   [204 136  89]\n",
      "   [245 177 132]\n",
      "   ..., \n",
      "   [118  49  20]\n",
      "   [120  50  24]\n",
      "   [120  50  24]]]]\n",
      "[253 231 194 253 231 194 253 231 194 253]\n"
     ]
    }
   ],
   "source": [
    "flattened = data.ravel()\n",
    "print(data[:1])\n",
    "print(flattened[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first convert our N x H x W x C dimensional array into a 1 dimensional array.  The values of this array will be based on the last dimensions order.  So we'll have: [<font color='red'>251</font>, <font color='green'>238</font>, <font color='blue'>205</font>, <font color='red'>251</font>, <font color='green'>238</font>, <font color='blue'>206</font>, <font color='red'>253</font>, <font color='green'>240</font>, <font color='blue'>207</font>, ...]\n",
    "\n",
    "We can visualize what the \"distribution\", or range and frequency of possible values are.  This is a very useful thing to know.  It tells us whether our data is predictable or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 216804.,  117821.,   99125.,   71049.,   66478.,   62066.,\n",
       "          62528.,   58085.,   56686.,   56114.,   56848.,   58551.,\n",
       "          63168.,   61086.,   59193.,   59305.,   62526.,   63596.,\n",
       "          62285.,   65061.,   63389.,   61989.,   61411.,   60742.,\n",
       "          60464.,   60307.,   59074.,   59312.,   60353.,   64807.,\n",
       "          67305.,   61667.,   59906.,   60546.,   62017.,   62268.,\n",
       "          63030.,   62799.,   62396.,   62609.,   59541.,   58918.,\n",
       "          57822.,   61451.,   63481.,   57782.,   57212.,   56516.,\n",
       "          56039.,   58385.,   56252.,   55089.,   54677.,   52066.,\n",
       "          51012.,   48826.,   47602.,   46064.,   45351.,   43735.,\n",
       "          42849.,   42903.,   42571.,   41789.,   42412.,   42705.,\n",
       "          42982.,   43107.,   43372.,   43416.,   43323.,   42808.,\n",
       "          42253.,   42302.,   42008.,   41846.,   42800.,   42476.,\n",
       "          42435.,   42405.,   42584.,   42612.,   42671.,   42344.,\n",
       "          42610.,   42023.,   42214.,   41700.,   41302.,   41160.,\n",
       "          41885.,   42650.,   41703.,   42498.,   41983.,   42702.,\n",
       "          42735.,   43540.,   42428.,   42376.,   42394.,   41547.,\n",
       "          42188.,   41566.,   40389.,   41123.,   40529.,   41291.,\n",
       "          42112.,   41069.,   41266.,   41475.,   41254.,   41075.,\n",
       "          41171.,   41432.,   41208.,   41324.,   41922.,   41582.,\n",
       "          42013.,   42234.,   42460.,   42677.,   42786.,   41815.,\n",
       "          41975.,   42119.,   41887.,   41585.,   41084.,   40260.,\n",
       "          40819.,   40261.,   40525.,   40688.,   40924.,   41340.,\n",
       "          42065.,   41707.,   42271.,   41754.,   42675.,   42349.,\n",
       "          43355.,   44380.,   44117.,   43332.,   42148.,   41166.,\n",
       "          41503.,   41165.,   40511.,   40550.,   40868.,   40656.,\n",
       "          40710.,   40901.,   41201.,   41412.,   41278.,   41196.,\n",
       "          42040.,   40484.,   41872.,   40003.,   39540.,   39412.,\n",
       "          39419.,   39000.,   39120.,   41259.,   40142.,   39750.,\n",
       "          39523.,   39662.,   40226.,   40471.,   40539.,   41334.,\n",
       "          40587.,   39869.,   41072.,   42369.,   38908.,   36865.,\n",
       "          36743.,   37041.,   37979.,   37332.,   36199.,   37241.,\n",
       "          38136.,   38718.,   40154.,   40233.,   40927.,   42931.,\n",
       "          42440.,   44739.,   43546.,   47023.,   45519.,   43844.,\n",
       "          39307.,   38330.,   38893.,   39873.,   38623.,   39838.,\n",
       "          40250.,   40881.,   39237.,   39683.,   38854.,   38480.,\n",
       "          40170.,   40491.,   41457.,   39982.,   38166.,   37335.,\n",
       "          36543.,   37362.,   36063.,   37213.,   38517.,   39497.,\n",
       "          39859.,   37243.,   38053.,   36426.,   32394.,   37110.,\n",
       "          32811.,   31258.,   27632.,   35700.,   30117.,   29352.,\n",
       "          28333.,   29947.,   27756.,   21400.,   20086.,   20511.,\n",
       "          23938.,   22960.,   24523.,   23963.,   28714.,   30620.,\n",
       "          30354.,   41291.,  149885.]),\n",
       " array([   0.,    1.,    2.,    3.,    4.,    5.,    6.,    7.,    8.,\n",
       "           9.,   10.,   11.,   12.,   13.,   14.,   15.,   16.,   17.,\n",
       "          18.,   19.,   20.,   21.,   22.,   23.,   24.,   25.,   26.,\n",
       "          27.,   28.,   29.,   30.,   31.,   32.,   33.,   34.,   35.,\n",
       "          36.,   37.,   38.,   39.,   40.,   41.,   42.,   43.,   44.,\n",
       "          45.,   46.,   47.,   48.,   49.,   50.,   51.,   52.,   53.,\n",
       "          54.,   55.,   56.,   57.,   58.,   59.,   60.,   61.,   62.,\n",
       "          63.,   64.,   65.,   66.,   67.,   68.,   69.,   70.,   71.,\n",
       "          72.,   73.,   74.,   75.,   76.,   77.,   78.,   79.,   80.,\n",
       "          81.,   82.,   83.,   84.,   85.,   86.,   87.,   88.,   89.,\n",
       "          90.,   91.,   92.,   93.,   94.,   95.,   96.,   97.,   98.,\n",
       "          99.,  100.,  101.,  102.,  103.,  104.,  105.,  106.,  107.,\n",
       "         108.,  109.,  110.,  111.,  112.,  113.,  114.,  115.,  116.,\n",
       "         117.,  118.,  119.,  120.,  121.,  122.,  123.,  124.,  125.,\n",
       "         126.,  127.,  128.,  129.,  130.,  131.,  132.,  133.,  134.,\n",
       "         135.,  136.,  137.,  138.,  139.,  140.,  141.,  142.,  143.,\n",
       "         144.,  145.,  146.,  147.,  148.,  149.,  150.,  151.,  152.,\n",
       "         153.,  154.,  155.,  156.,  157.,  158.,  159.,  160.,  161.,\n",
       "         162.,  163.,  164.,  165.,  166.,  167.,  168.,  169.,  170.,\n",
       "         171.,  172.,  173.,  174.,  175.,  176.,  177.,  178.,  179.,\n",
       "         180.,  181.,  182.,  183.,  184.,  185.,  186.,  187.,  188.,\n",
       "         189.,  190.,  191.,  192.,  193.,  194.,  195.,  196.,  197.,\n",
       "         198.,  199.,  200.,  201.,  202.,  203.,  204.,  205.,  206.,\n",
       "         207.,  208.,  209.,  210.,  211.,  212.,  213.,  214.,  215.,\n",
       "         216.,  217.,  218.,  219.,  220.,  221.,  222.,  223.,  224.,\n",
       "         225.,  226.,  227.,  228.,  229.,  230.,  231.,  232.,  233.,\n",
       "         234.,  235.,  236.,  237.,  238.,  239.,  240.,  241.,  242.,\n",
       "         243.,  244.,  245.,  246.,  247.,  248.,  249.,  250.,  251.,\n",
       "         252.,  253.,  254.,  255.]),\n",
       " <a list of 255 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6ejpFRUUkJSVFu6kjcvDgQT799FNSUlLYuXMnwBXrOXz4MFVVVUyfPp0NGzaw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\niC3+P5YBkFnBRWWnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113158470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(flattened.ravel(), 255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last line is saying give me a histogram of every value in the vector, and use 255 bins.  Each bin is grouping a range of values.  The bars of each bin describe the frequency, or how many times anything within that range of values appears.In other words, it is telling us if there is something that seems to happen more than anything else.  If there is, it is likely that a neural network will take advantage of that.\n",
    "\n",
    "\n",
    "<a name=\"histogram-equalization\"></a>\n",
    "## Histogram Equalization\n",
    "\n",
    "The mean of our dataset looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    2.,     0.,     0.,     0.,     2.,    18.,    30.,    23.,\n",
       "           42.,    42.,    43.,    41.,    51.,    48.,    30.,    52.,\n",
       "           58.,    59.,    67.,    67.,    71.,    83.,    94.,   110.,\n",
       "          110.,   107.,   134.,   103.,   109.,   160.,   198.,   206.,\n",
       "          211.,   265.,   306.,   289.,   344.,   335.,   361.,   382.,\n",
       "          402.,   390.,   400.,   497.,   544.,   594.,   663.,   679.,\n",
       "          749.,   803.,   847.,   882.,   886.,   938.,   817.,   857.,\n",
       "          801.,   825.,   831.,   896.,   875.,   861.,   843.,   870.,\n",
       "          842.,   897.,   863.,   861.,   906.,   939.,   905.,   935.,\n",
       "          946.,   972.,  1004.,  1064.,  1079.,  1124.,  1162.,  1103.,\n",
       "         1129.,  1108.,  1085.,  1079.,  1051.,  1155.,  1068.,  1093.,\n",
       "         1170.,  1171.,  1146.,  1213.,  1146.,  1174.,  1211.,  1229.,\n",
       "         1173.,  1196.,  1216.,  1150.,  1255.,  1373.,  1344.,  1445.,\n",
       "         1554.,  1554.,  1553.,  1478.,  1506.,  1469.,  1546.,  1626.,\n",
       "         1659.,  1568.,  1548.,  1559.,  1576.,  1376.,  1283.,  1189.,\n",
       "         1151.,  1093.,  1046.,  1068.,  1021.,  1059.,   985.,   927.,\n",
       "          801.,   696.,   566.,   508.,   448.,   399.,   352.,   371.,\n",
       "          343.,   345.,   321.,   285.,   293.,   283.,   261.,   244.,\n",
       "          218.,   221.,   263.,   210.,   208.,   202.,   191.,   173.,\n",
       "          209.,   182.,   208.,   199.,   196.,   185.,   197.,   172.,\n",
       "          188.,   147.,   157.,   174.,   147.,   168.,   138.,   140.,\n",
       "          130.,   102.,   104.,    99.,   117.,   103.,   113.,   112.,\n",
       "           86.,    93.,    84.,    77.,    95.,    88.,    89.,    72.,\n",
       "           64.,    71.,    63.,    58.,    56.,    52.,    60.,    57.,\n",
       "           55.,    59.,    57.,    54.,    66.,    60.,    65.,    72.,\n",
       "           67.,    75.,    83.,    91.,    84.,    83.,    89.,    78.,\n",
       "           83.,    85.,   102.,    82.,    77.,    89.,   111.,   101.,\n",
       "           89.,   100.,   111.,   109.,    97.,    83.,    80.,    72.,\n",
       "           85.,   108.,   108.,    91.,    80.,    83.,    82.,    86.,\n",
       "           96.,    98.,    77.,    73.,    74.,    64.,    63.,    56.,\n",
       "           49.,    62.,    50.,    52.,    64.,    64.,    64.,    77.,\n",
       "           50.,    33.,    26.,    30.,    10.,     8.,     7.]),\n",
       " array([  43.25      ,   43.95035294,   44.65070588,   45.35105882,\n",
       "          46.05141176,   46.75176471,   47.45211765,   48.15247059,\n",
       "          48.85282353,   49.55317647,   50.25352941,   50.95388235,\n",
       "          51.65423529,   52.35458824,   53.05494118,   53.75529412,\n",
       "          54.45564706,   55.156     ,   55.85635294,   56.55670588,\n",
       "          57.25705882,   57.95741176,   58.65776471,   59.35811765,\n",
       "          60.05847059,   60.75882353,   61.45917647,   62.15952941,\n",
       "          62.85988235,   63.56023529,   64.26058824,   64.96094118,\n",
       "          65.66129412,   66.36164706,   67.062     ,   67.76235294,\n",
       "          68.46270588,   69.16305882,   69.86341176,   70.56376471,\n",
       "          71.26411765,   71.96447059,   72.66482353,   73.36517647,\n",
       "          74.06552941,   74.76588235,   75.46623529,   76.16658824,\n",
       "          76.86694118,   77.56729412,   78.26764706,   78.968     ,\n",
       "          79.66835294,   80.36870588,   81.06905882,   81.76941176,\n",
       "          82.46976471,   83.17011765,   83.87047059,   84.57082353,\n",
       "          85.27117647,   85.97152941,   86.67188235,   87.37223529,\n",
       "          88.07258824,   88.77294118,   89.47329412,   90.17364706,\n",
       "          90.874     ,   91.57435294,   92.27470588,   92.97505882,\n",
       "          93.67541176,   94.37576471,   95.07611765,   95.77647059,\n",
       "          96.47682353,   97.17717647,   97.87752941,   98.57788235,\n",
       "          99.27823529,   99.97858824,  100.67894118,  101.37929412,\n",
       "         102.07964706,  102.78      ,  103.48035294,  104.18070588,\n",
       "         104.88105882,  105.58141176,  106.28176471,  106.98211765,\n",
       "         107.68247059,  108.38282353,  109.08317647,  109.78352941,\n",
       "         110.48388235,  111.18423529,  111.88458824,  112.58494118,\n",
       "         113.28529412,  113.98564706,  114.686     ,  115.38635294,\n",
       "         116.08670588,  116.78705882,  117.48741176,  118.18776471,\n",
       "         118.88811765,  119.58847059,  120.28882353,  120.98917647,\n",
       "         121.68952941,  122.38988235,  123.09023529,  123.79058824,\n",
       "         124.49094118,  125.19129412,  125.89164706,  126.592     ,\n",
       "         127.29235294,  127.99270588,  128.69305882,  129.39341176,\n",
       "         130.09376471,  130.79411765,  131.49447059,  132.19482353,\n",
       "         132.89517647,  133.59552941,  134.29588235,  134.99623529,\n",
       "         135.69658824,  136.39694118,  137.09729412,  137.79764706,\n",
       "         138.498     ,  139.19835294,  139.89870588,  140.59905882,\n",
       "         141.29941176,  141.99976471,  142.70011765,  143.40047059,\n",
       "         144.10082353,  144.80117647,  145.50152941,  146.20188235,\n",
       "         146.90223529,  147.60258824,  148.30294118,  149.00329412,\n",
       "         149.70364706,  150.404     ,  151.10435294,  151.80470588,\n",
       "         152.50505882,  153.20541176,  153.90576471,  154.60611765,\n",
       "         155.30647059,  156.00682353,  156.70717647,  157.40752941,\n",
       "         158.10788235,  158.80823529,  159.50858824,  160.20894118,\n",
       "         160.90929412,  161.60964706,  162.31      ,  163.01035294,\n",
       "         163.71070588,  164.41105882,  165.11141176,  165.81176471,\n",
       "         166.51211765,  167.21247059,  167.91282353,  168.61317647,\n",
       "         169.31352941,  170.01388235,  170.71423529,  171.41458824,\n",
       "         172.11494118,  172.81529412,  173.51564706,  174.216     ,\n",
       "         174.91635294,  175.61670588,  176.31705882,  177.01741176,\n",
       "         177.71776471,  178.41811765,  179.11847059,  179.81882353,\n",
       "         180.51917647,  181.21952941,  181.91988235,  182.62023529,\n",
       "         183.32058824,  184.02094118,  184.72129412,  185.42164706,\n",
       "         186.122     ,  186.82235294,  187.52270588,  188.22305882,\n",
       "         188.92341176,  189.62376471,  190.32411765,  191.02447059,\n",
       "         191.72482353,  192.42517647,  193.12552941,  193.82588235,\n",
       "         194.52623529,  195.22658824,  195.92694118,  196.62729412,\n",
       "         197.32764706,  198.028     ,  198.72835294,  199.42870588,\n",
       "         200.12905882,  200.82941176,  201.52976471,  202.23011765,\n",
       "         202.93047059,  203.63082353,  204.33117647,  205.03152941,\n",
       "         205.73188235,  206.43223529,  207.13258824,  207.83294118,\n",
       "         208.53329412,  209.23364706,  209.934     ,  210.63435294,\n",
       "         211.33470588,  212.03505882,  212.73541176,  213.43576471,\n",
       "         214.13611765,  214.83647059,  215.53682353,  216.23717647,\n",
       "         216.93752941,  217.63788235,  218.33823529,  219.03858824,\n",
       "         219.73894118,  220.43929412,  221.13964706,  221.84      ]),\n",
       " <a list of 255 Patch objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5kXKzks/n4/Dhw/T392OxWLj//vt54IEHGBwc5ODBg/T29rJkyRJ27txJSUkJ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\niIgkUHAQEZEE/x+lcEOsrlxCAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6b82b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mean_img.ravel(), 255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we subtract an image by our mean image, we remove all of this information from it.  And that means that the rest of the information is really what is important for describing what is unique about it.\n",
    "\n",
    "Let's try and compare the histogram before and after \"normalizing our data\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ad4b7b8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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376+ZM2dqxYoVio6O1pgxYyQV3XVq06ZNGjlypAIDAzV8+HBJUkhIiG655RaN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\nyP4u6EKxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a86f5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 20\n",
    "fig, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True, sharex=True)\n",
    "axs[0].hist((data[0]).ravel(), bins)\n",
    "axs[0].set_title('img distribution')\n",
    "axs[1].hist((mean_img).ravel(), bins)\n",
    "axs[1].set_title('mean distribution')\n",
    "axs[2].hist((data[0] - mean_img).ravel(), bins)\n",
    "axs[2].set_title('(img - mean) distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we can see from the histograms is the original image's distribution of values from 0 - 255.  The mean image's data distribution is mostly centered around the value 100.  When we look at the difference of the original image and the mean image as a histogram, we can see that the distribution is now centered around 0.  What we are seeing is the distribution of values that were above the mean image's intensity, and which were below it.  Let's take it one step further and complete the normalization by dividing by the standard deviation of our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11b1c8c18>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sKLCsi1+npKSYnj17mvr165uAgAATExNjnn76aY+bvl9++WXTsGFD4+fn567f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\nSEhpMi8iIiIiElL/AZdwT8oYU118AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0c6cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(12, 6), sharey=True, sharex=True)\n",
    "axs[0].hist((data[0] - mean_img).ravel(), bins)\n",
    "axs[0].set_title('(img - mean) distribution')\n",
    "axs[1].hist((std_img).ravel(), bins)\n",
    "axs[1].set_title('std deviation distribution')\n",
    "axs[2].hist(((data[0] - mean_img) / std_img).ravel(), bins)\n",
    "axs[2].set_title('((img - mean) / std_dev) distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our data has been squished into a peak!  We'll have to look at it on a different scale to see what's going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 5)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axs[2].set_xlim([-150, 150])\n",
    "axs[2].set_xlim([-100, 100])\n",
    "axs[2].set_xlim([-50, 50])\n",
    "axs[2].set_xlim([-10, 10])\n",
    "axs[2].set_xlim([-5, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we can see is that the data is in the range of -3 to 3, with the bulk of the data centered around -1 to 1.  This is the effect of normalizing our data: most of the data will be around 0, where some deviations of it will follow between -3 to 3.\n",
    "\n",
    "If our data does not end up looking like this, then we should either (1): get much more data to calculate our mean/std deviation, or (2): either try another method of normalization, such as scaling the values between 0 to 1, or -1 to 1, or possibly not bother with normalization at all.  There are other options that one could explore, including different types of normalization such as local contrast normalization for images or PCA based normalization but we won't have time to get into those in this course.\n",
    "\n",
    "<a name=\"tensorflow-basics\"></a>\n",
    "# Tensorflow Basics\n",
    "\n",
    "Let's now switch gears and start working with Google's Library for Numerical Computation, TensorFlow.  This library can do most of the things we've done so far.  However, it has a very different approach for doing so.  And it can do a whole lot more cool stuff which we'll eventually get into.  The major difference to take away from the remainder of this session is that instead of computing things immediately, we first define things that we want to compute later using what's called a `Graph`.  Everything in Tensorflow takes place in a computational graph and running and evaluating anything in the graph requires a `Session`.  Let's take a look at how these both work and then we'll get into the benefits of why this is useful:\n",
    "\n",
    "<a name=\"variables\"></a>\n",
    "## Variables\n",
    "\n",
    "We're first going to import the tensorflow library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at how we might create a range of numbers.  Using numpy, we could for instance use the linear space function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.         -2.93939394 -2.87878788 -2.81818182 -2.75757576 -2.6969697\n",
      " -2.63636364 -2.57575758 -2.51515152 -2.45454545 -2.39393939 -2.33333333\n",
      " -2.27272727 -2.21212121 -2.15151515 -2.09090909 -2.03030303 -1.96969697\n",
      " -1.90909091 -1.84848485 -1.78787879 -1.72727273 -1.66666667 -1.60606061\n",
      " -1.54545455 -1.48484848 -1.42424242 -1.36363636 -1.3030303  -1.24242424\n",
      " -1.18181818 -1.12121212 -1.06060606 -1.         -0.93939394 -0.87878788\n",
      " -0.81818182 -0.75757576 -0.6969697  -0.63636364 -0.57575758 -0.51515152\n",
      " -0.45454545 -0.39393939 -0.33333333 -0.27272727 -0.21212121 -0.15151515\n",
      " -0.09090909 -0.03030303  0.03030303  0.09090909  0.15151515  0.21212121\n",
      "  0.27272727  0.33333333  0.39393939  0.45454545  0.51515152  0.57575758\n",
      "  0.63636364  0.6969697   0.75757576  0.81818182  0.87878788  0.93939394\n",
      "  1.          1.06060606  1.12121212  1.18181818  1.24242424  1.3030303\n",
      "  1.36363636  1.42424242  1.48484848  1.54545455  1.60606061  1.66666667\n",
      "  1.72727273  1.78787879  1.84848485  1.90909091  1.96969697  2.03030303\n",
      "  2.09090909  2.15151515  2.21212121  2.27272727  2.33333333  2.39393939\n",
      "  2.45454545  2.51515152  2.57575758  2.63636364  2.6969697   2.75757576\n",
      "  2.81818182  2.87878788  2.93939394  3.        ]\n",
      "(100,)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "x = np.linspace(-3.0, 3.0, 100)\n",
    "\n",
    "# Immediately, the result is given to us.  An array of 100 numbers equally spaced from -3.0 to 3.0.\n",
    "print(x)\n",
    "\n",
    "# We know from numpy arrays that they have a `shape`, in this case a 1-dimensional array of 100 values\n",
    "print(x.shape)\n",
    "\n",
    "# and a `dtype`, in this case float64, or 64 bit floating point values.\n",
    "print(x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"tensors\"></a>\n",
    "## Tensors\n",
    "\n",
    "In tensorflow, we could try to do the same thing using their linear space function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"LinSpace_1:0\", shape=(100,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.linspace(-3.0, 3.0, 100)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of a `numpy.array`, we are returned a `tf.Tensor`.  The name of it is \"LinSpace:0\".  Wherever we see this colon 0, that just means the output of.  So the name of this Tensor is saying, the output of LinSpace.\n",
    "\n",
    "Think of `tf.Tensor`s the same way as you would the `numpy.array`.  It is described by its `shape`, in this case, only 1 dimension of 100 values.  And it has a `dtype`, in this case, `float32`.  But *unlike* the `numpy.array`, there are no values printed here!  That's because it actually hasn't computed its values yet.  Instead, it just refers to the output of a `tf.Operation` which has been already been added to Tensorflow's default computational graph.  The result of that operation is the tensor that we are returned.\n",
    "\n",
    "<a name=\"graphs\"></a>\n",
    "## Graphs\n",
    "\n",
    "Let's try and inspect the underlying graph.  We can request the \"default\" graph where all of our operations have been added:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"operations\"></a>\n",
    "## Operations\n",
    "\n",
    "And from this graph, we can get a list of all the operations that have been added, and print out their names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LinSpace/start',\n",
       " 'LinSpace/stop',\n",
       " 'LinSpace/num',\n",
       " 'LinSpace',\n",
       " 'Reshape/tensor',\n",
       " 'Reshape/shape',\n",
       " 'Reshape',\n",
       " 'LinSpace_1/start',\n",
       " 'LinSpace_1/stop',\n",
       " 'LinSpace_1/num',\n",
       " 'LinSpace_1']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[op.name for op in g.get_operations()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So Tensorflow has named each of our operations to generally reflect what they are doing.  There are a few parameters that are all prefixed by LinSpace, and then the last one which is the operation which takes all of the parameters and creates an output for the linspace.\n",
    "\n",
    "<a name=\"tensor\"></a>\n",
    "## Tensor\n",
    "\n",
    "We can request the output of any operation, which is a tensor, by asking the graph for the tensor's name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'LinSpace:0' shape=(100,) dtype=float32>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.get_tensor_by_name('LinSpace' + ':0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What I've done is asked for the `tf.Tensor` that comes from the operation \"LinSpace\".  So remember, the result of a `tf.Operation` is a `tf.Tensor`.  Remember that was the same name as the tensor `x` we created before.\n",
    "\n",
    "<a name=\"sessions\"></a>\n",
    "## Sessions\n",
    "\n",
    "In order to actually compute anything in tensorflow, we need to create a `tf.Session`.  The session is responsible for evaluating the `tf.Graph`.  Let's see how this works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.         -2.939394   -2.87878799 -2.81818175 -2.75757575 -2.69696975\n",
      " -2.63636351 -2.5757575  -2.5151515  -2.4545455  -2.3939395  -2.33333325\n",
      " -2.27272725 -2.21212125 -2.15151501 -2.090909   -2.030303   -1.969697\n",
      " -1.90909088 -1.84848475 -1.78787875 -1.72727275 -1.66666663 -1.6060605\n",
      " -1.5454545  -1.4848485  -1.42424238 -1.36363626 -1.30303025 -1.24242425\n",
      " -1.18181813 -1.12121201 -1.060606   -1.         -0.939394   -0.87878776\n",
      " -0.81818175 -0.75757575 -0.69696951 -0.63636351 -0.5757575  -0.5151515\n",
      " -0.4545455  -0.39393926 -0.33333325 -0.27272725 -0.21212101 -0.15151501\n",
      " -0.090909   -0.030303    0.030303    0.09090924  0.15151525  0.21212125\n",
      "  0.27272749  0.33333349  0.3939395   0.4545455   0.5151515   0.57575774\n",
      "  0.63636374  0.69696975  0.75757599  0.81818199  0.87878799  0.939394    1.\n",
      "  1.060606    1.12121201  1.18181849  1.24242449  1.30303049  1.36363649\n",
      "  1.4242425   1.4848485   1.5454545   1.60606098  1.66666698  1.72727299\n",
      "  1.78787899  1.84848499  1.909091    1.969697    2.030303    2.090909\n",
      "  2.15151548  2.21212149  2.27272749  2.33333349  2.3939395   2.4545455\n",
      "  2.5151515   2.57575798  2.63636398  2.69696999  2.75757599  2.81818199\n",
      "  2.87878799  2.939394    3.        ]\n",
      "[-3.         -2.939394   -2.87878799 -2.81818175 -2.75757575 -2.69696975\n",
      " -2.63636351 -2.5757575  -2.5151515  -2.4545455  -2.3939395  -2.33333325\n",
      " -2.27272725 -2.21212125 -2.15151501 -2.090909   -2.030303   -1.969697\n",
      " -1.90909088 -1.84848475 -1.78787875 -1.72727275 -1.66666663 -1.6060605\n",
      " -1.5454545  -1.4848485  -1.42424238 -1.36363626 -1.30303025 -1.24242425\n",
      " -1.18181813 -1.12121201 -1.060606   -1.         -0.939394   -0.87878776\n",
      " -0.81818175 -0.75757575 -0.69696951 -0.63636351 -0.5757575  -0.5151515\n",
      " -0.4545455  -0.39393926 -0.33333325 -0.27272725 -0.21212101 -0.15151501\n",
      " -0.090909   -0.030303    0.030303    0.09090924  0.15151525  0.21212125\n",
      "  0.27272749  0.33333349  0.3939395   0.4545455   0.5151515   0.57575774\n",
      "  0.63636374  0.69696975  0.75757599  0.81818199  0.87878799  0.939394    1.\n",
      "  1.060606    1.12121201  1.18181849  1.24242449  1.30303049  1.36363649\n",
      "  1.4242425   1.4848485   1.5454545   1.60606098  1.66666698  1.72727299\n",
      "  1.78787899  1.84848499  1.909091    1.969697    2.030303    2.090909\n",
      "  2.15151548  2.21212149  2.27272749  2.33333349  2.3939395   2.4545455\n",
      "  2.5151515   2.57575798  2.63636398  2.69696999  2.75757599  2.81818199\n",
      "  2.87878799  2.939394    3.        ]\n"
     ]
    }
   ],
   "source": [
    "# We're first going to create a session:\n",
    "sess = tf.Session()\n",
    "\n",
    "# Now we tell our session to compute anything we've created in the tensorflow graph.\n",
    "computed_x = sess.run(x)\n",
    "print(computed_x)\n",
    "\n",
    "# Alternatively, we could tell the previous Tensor to evaluate itself using this session:\n",
    "computed_x = x.eval(session=sess)\n",
    "print(computed_x)\n",
    "\n",
    "# We can close the session after we're done like so:\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also explicitly tell the session which graph we want to manage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session(graph=g)\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, it grabs the default graph.  But we could have created a new graph like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g2 = tf.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then used this graph only in our session.\n",
    "\n",
    "To simplify things, since we'll be working in iPython's interactive console, we can create an `tf.InteractiveSession`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.        , -2.939394  , -2.87878799, -2.81818175, -2.75757575,\n",
       "       -2.69696975, -2.63636351, -2.5757575 , -2.5151515 , -2.4545455 ,\n",
       "       -2.3939395 , -2.33333325, -2.27272725, -2.21212125, -2.15151501,\n",
       "       -2.090909  , -2.030303  , -1.969697  , -1.90909088, -1.84848475,\n",
       "       -1.78787875, -1.72727275, -1.66666663, -1.6060605 , -1.5454545 ,\n",
       "       -1.4848485 , -1.42424238, -1.36363626, -1.30303025, -1.24242425,\n",
       "       -1.18181813, -1.12121201, -1.060606  , -1.        , -0.939394  ,\n",
       "       -0.87878776, -0.81818175, -0.75757575, -0.69696951, -0.63636351,\n",
       "       -0.5757575 , -0.5151515 , -0.4545455 , -0.39393926, -0.33333325,\n",
       "       -0.27272725, -0.21212101, -0.15151501, -0.090909  , -0.030303  ,\n",
       "        0.030303  ,  0.09090924,  0.15151525,  0.21212125,  0.27272749,\n",
       "        0.33333349,  0.3939395 ,  0.4545455 ,  0.5151515 ,  0.57575774,\n",
       "        0.63636374,  0.69696975,  0.75757599,  0.81818199,  0.87878799,\n",
       "        0.939394  ,  1.        ,  1.060606  ,  1.12121201,  1.18181849,\n",
       "        1.24242449,  1.30303049,  1.36363649,  1.4242425 ,  1.4848485 ,\n",
       "        1.5454545 ,  1.60606098,  1.66666698,  1.72727299,  1.78787899,\n",
       "        1.84848499,  1.909091  ,  1.969697  ,  2.030303  ,  2.090909  ,\n",
       "        2.15151548,  2.21212149,  2.27272749,  2.33333349,  2.3939395 ,\n",
       "        2.4545455 ,  2.5151515 ,  2.57575798,  2.63636398,  2.69696999,\n",
       "        2.75757599,  2.81818199,  2.87878799,  2.939394  ,  3.        ], dtype=float32)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "x.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we didn't have to explicitly tell the `eval` function about our session.  We'll leave this session open for the rest of the lecture.\n",
    "\n",
    "<a name=\"tensor-shapes\"></a>\n",
    "## Tensor Shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n",
      "[100]\n"
     ]
    }
   ],
   "source": [
    "# We can find out the shape of a tensor like so:\n",
    "print(x.get_shape())\n",
    "\n",
    "# %% Or in a more friendly format\n",
    "print(x.get_shape().as_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"many-operations\"></a>\n",
    "## Many Operations\n",
    "\n",
    "Lets try a set of operations now.  We'll try to create a Gaussian curve.  This should resemble a normalized histogram where most of the data is centered around the mean of 0.  It's also sometimes refered to by the bell curve or normal curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The 1 dimensional gaussian takes two parameters, the mean value, and the standard deviation, which is commonly denoted by the name sigma.\n",
    "mean = 0.0\n",
    "sigma = 1.0\n",
    "\n",
    "# Don't worry about trying to learn or remember this formula.  I always have to refer to textbooks or check online for the exact formula.\n",
    "z = (tf.exp(tf.negative(tf.pow(x - mean, 2.0) /\n",
    "                   (2.0 * tf.pow(sigma, 2.0)))) *\n",
    "     (1.0 / (sigma * tf.sqrt(2.0 * 3.1415))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like before, amazingly, we haven't actually computed anything.  We *have just added a bunch of operations to Tensorflow's graph.  Whenever we want the value or output of this operation, we'll have to explicitly ask for the part of the graph we're interested in before we can see its result.  Since we've created an interactive session, we should just be able to say the name of the Tensor that we're interested in, and call the `eval` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x107237518>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y1itYw693OooKVBf3gpJi2PWh00nUOai2IHi9XqKjKyc783g8eL3eGr/AyZMn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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11da860f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = z.eval()\n",
    "plt.plot(res)\n",
    "# if nothing is drawn, and you are using ipython notebook, uncomment the next two lines:\n",
    "#%matplotlib inline\n",
    "#plt.plot(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"convolution\"></a>\n",
    "# Convolution\n",
    "\n",
    "<a name=\"creating-a-2-d-gaussian-kernel\"></a>\n",
    "## Creating a 2-D Gaussian Kernel\n",
    "\n",
    "Let's try creating a 2-dimensional Gaussian.  This can be done by multiplying a vector by its transpose.  If you aren't familiar with matrix math, I'll review a few important concepts.  This is about 98% of what neural networks do so if you're unfamiliar with this, then please stick with me through this and it'll be smooth sailing.  First, to multiply two matrices, their inner dimensions must agree, and the resulting matrix will have the shape of the outer dimensions.\n",
    "\n",
    "So let's say we have two matrices, X and Y.  In order for us to multiply them, X's columns must match Y's rows.  I try to remember it like so:\n",
    "<pre>\n",
    "    (X_rows, X_cols) x (Y_rows, Y_cols)\n",
    "      |       |           |      |\n",
    "      |       |___________|      |\n",
    "      |             ^            |\n",
    "      |     inner dimensions     |\n",
    "      |        must match        |\n",
    "      |                          |\n",
    "      |__________________________|\n",
    "                    ^\n",
    "           resulting dimensions\n",
    "         of matrix multiplication\n",
    "</pre>\n",
    "But our matrix is actually a vector, or a 1 dimensional matrix.  That means its dimensions are N x 1.  So to multiply them, we'd have:\n",
    "<pre>\n",
    "     (N,      1)    x    (1,     N)\n",
    "      |       |           |      |\n",
    "      |       |___________|      |\n",
    "      |             ^            |\n",
    "      |     inner dimensions     |\n",
    "      |        must match        |\n",
    "      |                          |\n",
    "      |__________________________|\n",
    "                    ^\n",
    "           resulting dimensions\n",
    "         of matrix multiplication\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11db90828>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XKFDktNFkKlnxqY5YAFsh2WuOs9wPEJQUXzrOLD6DVz43CPeNZkNWHmBSflrr0/9DlXoN\nENChzUsilSVaQ9Oav0bzFeaAdYjkvDg/M1j6zXj88cfx9NNPAwC22y1e+9rX4vnnn8cXvvAFvPWt\nbwUA3Lx5E5///OfPlYCyLDGCtbW+FJ/Sr5ry1zRGqr3pMQ8NIldvMQeBfK1h10h1rRRgFGAV0KgY\nbYNpm8MWwFHa5tr1iITtwnEOxylI1w6Nv/TOnO5t+vQt+6bxW9O325QXiv5LCq45D6U8LuU/Bd6S\ns/ZQpsiRtcRUeXmEcG2NHtyfHOQj+MpXvoIvf/nL+O7v/m584xvfwOOPPw4ggsU3v/nN+07MejnU\ndKA/jA+4WQsGNB4tIJwJ8JYAqbBJtT/1GZBvy6/mZZSTDqnsbsk90mfWzpXKJhfeSrCGBXAmkP13\nmcVQhsDZer5G3zlmEP0/mR1kyXGccE1qaqT31cyEGgOlfoj8fpBztdaILNRseHCyGgjOzs7wm7/5\nm3jPe96D7Xa7+gV37tzBnTt3xuNnnnkGN28+DeBm4Y41Cl6Kw9GXx186Xw43b34L9g3r0nHpnKSB\ndJ+kiSdBekTpGoCbb8a8h/GawLPkEKm1FtQCAYabb8K+8pd8DR5AUOS6wqRwwDSKMSNO6SG86aJ0\njh5PiYrl4n+u+FApo6TzYOel6yVZjvPss8+O+zdu3MCNGzcArAQC5xw++tGP4kd/9Efx5je/GUBk\nAV//+tfH7Ste8QrxXvqyLLdv/wtu3brNYpZKZikOWBypKpOOaY271iyI4datf8S8Cua1e60FQGIG\neZ9+A+YMILNa/grJEsn7CkAL3Pp/yfn8uaU+SQU8WsUIaPlbcgoOmOtnruUTI7j1/5BzlAE44XzP\n4ozORGqf8wdIgSaOv8Sz+z05H2v0W7f+ofCxErDQ8RM5A2vHYNdKyl4Dnygf+chNPPPMM8K9K4Hg\nd3/3d/HUU0/hHe94x3juB37gB3D79m28613vwu3bt/GmN71pzaMqwhW3Fk+ypTT2QUI6pvHPE6Q2\nf8k3UKL/UotBTpqaK6zFZNJKWFPa1+meY5ZUyXKRCArNrhLrzULLOFA3EbgDkJsGDaLfgOoi3c/M\nPm/5L/dqAoMgfUiJCvF5EvKHcKl5/ZeENxvyZ1DRmIMBvb7kcMxgc5gsAsGXvvQl/MVf/AVe97rX\n4Zd+6ZeglMJP/dRP4V3vehc+9rGP4bOf/Sxe9apX4YMf/ODBL78YkWr+ElDcj/Ir7GtRzTnItVPy\nE6QCxF9Hb+VAwDGEN0DkJGUg4FhFfQxLJsehjKBI4bFv41MAyPsWExAMiDU+Vf4SENBrHoAPpMKu\n0aAcMtIsfWimW7wVYg0QcJFaFnIG5meevxXgUFkEgu/5nu/Bpz/9afHahz/84QtP0GHC+Sw9fxG1\nf9YiXuLotVILQQ0cSJopA+DK3mDuAJdYgS3EoUCgsQ8iJRDggLBGSiY2P+Y1PWcGFAgyCPSYlN9i\nzgxyWntybQDg1OTzC1SpSv+YOw+lDwSJw52J5wECYGIhHATou84DBtQ8WicHtRo8GFlT9Ug+gbXP\n5iV9bdySGVBjAhIAUA0loEJdFlTxl4BgTZx8/YgllbcsSAAgMYOarGEC3DSQ7H+HuWlAs5CCB1V6\nXqnnb+0x6fdokq9V2NIHc7qdmYFUEdWUkKfjUEVfayZoHNLacEmAYM0POhR1LwIESvs1c6AEDCwd\nqhC91MUgK3CLfWCoMYKSb2EJBNYwA8mxXgMDye6nW8oIJCDICp6Vnv6SgZwDS3sP4kik5aj2YWtE\nahYEpqbIktwPGOSPqjU90jiXHggkZ14tHo+z5Avg1+nxGnMgl6qsOdQ0WGICDdmS5yqVAuZKzB2D\nDcrgkPvF1K5TH0HJr8CtnhoY1GQNCORaWQIAut9iAoIGUYF7Ei9nPzUd+HFO+0D2A6K5MLIXDgZr\nHHjSNWrPUzHk4zWLS80A+v4cjysvf5e0v5S2ZWbwkIFgqZSVanLuIKTnagyAA0XNJ8DNAuosXMMG\nsram9+T/zZsFaWiFc1zhlzrPNeT11yCDALdUambCIYygBAi89U5iA1n5jzEpf4NJybOiW7I/sOPM\nGKTiMeuMpICQwb2mVKVWguyAyIiabRV6Xy5DedBRfp9US2t2jrcwSE2K/Jok65nBJWAEa+JJ93Gm\nwPdp7U9RtwYEXDOWnIM1JpDvza9Sc48+ZwClfYktcMDgcXISrkMGAt7UmMuplD1rgYCbALy1oAYA\nOTSI4NWRc9knwNNvsM8UJFznxUWplL5AmEHAfHk4KtLH09qcvoxmClduvp/vpUpcY8cSG+DPLaV7\nWc8ugY/gECkxgVJplUp5iQnUAkX+tUxAz5NHAWBJ+SVmQM8vXW8Q++dnIKiBQQ33ePbSsljqSESb\nBWk/nRoI5M5BLSIjoGYBZzA07TlOybch/cpccdN+D2NtfR5mINlQGQioaUBr+fw+OikKfzZ/dwaW\nB9OkeImBoISKku3PTQwab6mELAED9xdIbKBh28wE1BxLuNJKyr92u3SOMgIRDAL5NLKfj5MrY8+E\nzUI6EgWkfa9YHwE1Nwtys57Uya/HxAh6RFbQk33L9vOWW28lRkNDblbMwAVM/0zsxFMSWrPTDKLl\nR2ICOfMyCEi1N43Hr0t+gSVwqH/PQwKCJbOgVsvX7ikZtocygBLHpFotMQHitlYkKvX2UxCgYena\nphC3tN8iAgFvTcggMCqRTyEAOkDpAKUBpQOgApSK2/GbAFKbKoSg4tbngAQACnA6BgkEaEtADhQI\nGnY+g0CTttxMqP22UjEa+xrkb8o/TGIG1PlXKhdZaCXCa/uScuf71tn0+/fy9PI4HMnncgmBoFQF\nleLy+7j5wKsDzfYPBQLecsDb7oCxZSBHL9XcXLmp0tNrdLjxIazgWpi3MDAQUDYAxgHWAdpHEFAB\nOu1r5aF0rZkK8F4jBI3gVdqPgACvgcEADgiDAlxItb+aM4IOQK8m04ADAVV+CgIUZEutHzMGkBLM\nix1tUQAwAQH3tHtMir0WCKj9ocg5SvNpIkHOnbc5sWTGXEpGcKiUTII1zEIClBoIlJoIS0wgl0Y1\nf3R+BFVUXsNT5abgsDcWH3NA4OAhgUkL4DEAjQeaANV4aOuhjIe2Acp4KBugjYPSDkr7qPhIYKA8\nlApQ+RwrYCGfDVH5fdoGpH2vEbyBdwZhUAhOww8K3mmEXiMMGuh1BIFsBmQWkwEgK38GhgwCOW/p\ncUk3aX1Aj3lxmI0FonQuC20WoULjOXKOA4HUhMdBQEpYfg53UK7vLLRGrhAQaHa8FgSkeEs+AUnZ\nKRBwd326lxINydNfq+UpOPDJOWicLebMQHpOPnc9AG2Aahx0M0A3A4x1MMbBWA9tHIx20NpBw0Mj\nQCMBQN6Pqg3NvNs+xchXIyjkmGnrDVwCAzdoOGfgBgPfW/jeIvQAOj1nBI8h7u9SoGyAMgFKwigj\n4GRPKg6A7A8c9TaXAyprgCA/SAICySGYE1gzBTj1pwzgJQsESwpeYgSH0Pwcv2ZYSsDAGQGrihQw\nNhFKTKAEAhvhnAQEtftaAJuo9Gg9VBOgWgX7WAedQMDaAcYMMNrB6iEBgIdRDkYN0MrDjCrtGRBU\nGAGNpeK+g4YPBk4bOGXglIbXBoO3cI3B0Fg418D3JobOIPQa2JjICDqFOBsTJmaww5wJdIXfSn/b\nknXJi5nYtJhFAgLuLOSBmxa0FWGNzNAJZRTjH3M4QFwiIDiPgzDfd4itf2goAUEGAw2xt+BaECgF\nCQykMHMsemDjoNsBujVo1Cls62DNAKt7WD3AYoBVPQwSCMBBI25Nqs8pAMyZwL6hPbKBkTfo8Yle\nZSAwcEZjCA0GWAzeYvANBmcxdAaut3C9BdpN6gRl9kGgkPWiOcDNgrUy0zPFwEACgmzPrwECTlHW\nyIMYhyDLJQICQAaCkm+A7tfOLfkDqF+A7vNAgYDxUaXKPQZ5C0BJ4SXFl66N+2FkAaoNUK2D3g7Q\n7QBre9hmg2NzAmsHWD2gUT2sGmDRRzAYld/BYIBGZAQGbo8NxFzdZwR5S82DDC1eabj8FmUwwEQQ\nQINBW/ShwWAsem0xWIuhaWBaA3vdwTcWoTMIjcI4T6FV8i+R2MBaEOCm+9hMr8hEJ1l5LeaKGTD5\nhgx5UK71s2mREyIBAaX6tTIssQHuX3jJMAJJuI3/IFgDrV4kRyHvYszPocwGOBDUFP2ocP0IBZCI\nQKA2DrrtYTcD7KaHbXs0ukdrNK6ZeyMANCMAzAMFguwnSKqcPm3JNIh54DMLmDEDm56+/+ZeJUBo\nIiB0bYtGb7B5bIe+beB2Fr5rEHYm/RZV/zX8t66Rcc6C8aPmPSLHn0uZIVd26iOgD8jlNLdR8oQd\nqqySryCzjnrLzhq55EAgef1LDEEK9DplAVLtLwEB9Ujxe/Xc3UBvkfoC1EBgi/3an56ns/1uPLAN\nUBsPtXEwmwG27WDbDm3ToW17tOjQosU13EODnoBAn+tkAgTDqPiZFVDzIObgHAw4CHCzYNo3cOOb\nCBSpCQx6NOhNk9J5HdvtCbRpMdgWQxPgrEWwBqEJiRno6TeUmACtKHMRkCRX4rSypUAwzolIwcBj\nzg7yzwfm5gN9aCD3g8Rdw2z5BwR2/v5BALj0QFCTmnHI6f8axyCvZnj1o9k+OVUyBdb4AY6E4205\nqCMPtR1gNj3sdoBtejSmQ2s7tHqHDbpRqeZA0M8AIKulAfUTeKLGc0dhCQi4f2ACA2p4zN/ejymY\nhxYdrpkTNGpApwf0zYChbaJzsWkQrEUwuv7rs0itAiXJwEGBIJ8f10fJP5tGzOczQBhyM2cC1HwA\ni1vrdJSffTEKX5JLDAQ1+s/t/xIjKPkESmBBA/dOZdMgdxoi0Uq9+2pOQE776fG4BkCYrh8BOArQ\nRwP0UYdm06FtMwB0aFWHDXbYYDeq1jXcQ5uAgbMBDgQTIFAQoK5AGQh4M+LEBKawDwITEHSJvwyw\nMc3mXnRu2gGdH9A1LZRtgSbAG8Bpjb0FXvJxTNx+MakVqcACdwNQdjAqb2YFwAQQlA3QUHIg0o5F\nkq8go5rU6YhL7do6uQRAUHIQUo/Pef0CUklYYga07yo9JtUNJxFL4wg4MFBToAQCdEGQbQaAAXa7\nQ7Pt0NodNqbDRu3QIoJA3jaI5sE13EWb1G2qd+esYFJXz3wFU4+AyQBA2ot5GYswv0NjgJ0BQn7r\nZKDk1LQJCtqJESQWs8Mm+jfMANMOMMqhTz0dvbbw2ka/wZJjkPe7ocq9Zi4Ffg0UEGitTWvuWvni\now0PLdsUFKivgFMZ+sHLcgmAAJCVVfIDnOe5hzAGyW+QNV7PH0n773PfQGlkYIkJUF+AGCIQmKMz\ntO0Om80OW70bGUAO7Vi/RsW/jnukzu1GFtAwIMhE3o6tB25sPch1viS5gXFqLTAjEOT9EhB0oyej\nRYcePZqRxYzeBDXAGAdtXOwVqT2C8YDewmszZwJLxYD7AfgISVqZ8+HUFmRcAnUO0rkGLLmJl6la\n+TuU8ktKzsEgP3/9sy8BEEgOEek4n1tTq9d8A5KiL5kImQ2k9+eoNQZQMw8kE2G2dFgAjgLUkYc+\ncjDbHs3mDO1mh409w0bvsFUUBM7StpuBAWUE8dxUF0tAwMMEBFQC2dsHAhr2gaAZ+UnkAz26BAcd\n2hEILHoYxfwXysE0DkZ5dCFAqwCnLLw2CKO5UAGGUq0vnecMfxxIRcthBgRqx9dMT24ycF/BWmrP\n43KbiB6vNxkuARAcImtB4CICNRGEsQSSSSABQw0MuDmQgeA4AFsPdTTAHHVotzts2jNsmzNs1Bm2\n4GzgTGQGEyPoZ6ygRXcgEKxnBDUg6GawREGgQ5P2jnFvbMkwNF3KwRgPk8dEGI9ObxB0i6As4pBJ\nVW4xqNH/knlAQ678Z30Lsv1uMDEBaWCSFLivgJieVXlJzkewltfR/VrIcTgdO4RBSI3V6TkcBDgY\nlACg1FlozxwIwHGAOnLQxwPstkO72aHd7HBkTrHVZ9iqM2yZ4mcg2I5AsEOTGMFmBICJEWRAOBQI\neN1zCBD0JAV0u0OLJrGByAhOmN+CAIHy0CEOloq/Iw6QGnSAVzamK6hYe1Op1fg1QOCzLA+YmhRH\nAAApM3wVVNi+AAAgAElEQVTBxiUgoDSegsOacp7jLYHCOlbwEIGAekbvJ07pHl04VwOB0mAjNTcF\nJcwodS3mii85CSkQbD308YDmeBeZgI3mwFad4kidYYtTHM2AQGIIO7TocR13R5OBAoHFMPMZLAFB\nbjmgBZWPSJCekP0FWek32BFG0CRzYJNcnJGhHCcgsBwIxvQE5IlTNAKUiXMo9ABcUNGGp0AgOQZr\nJgEFAMlKRLo2mx49NytKjoe1TsND/QXUD1BrTViKM33BQ5KSA/A8TsIldnAII+AtBsQkkLoQl0yE\nWl+CWd+AMAKBOormgN0Sc8CcYavPcITTEQS2mFgB31Lz4DruYpsYAvUPNIQZzFv7pyZEExymsYSx\n3qcyb2DUcGpiAANRZQfDvBMNYwM9dsl5mVsNSs2aQBwWDRVbMJRKoABME6MEG+dDAJKyq7pPoLTy\nUp5DkcfNzxj7FgBzH0EOktOwBAilsluSEivg+1IcWS6Rj6BEf/K1EjPgyn4eEKBgQM2KJPkUH1pc\nAoRar8JZPwHMTAR95KJPYBOZwNac4Uid4ginIxBsk9JLgQJC9hHQvgXURzDvbTj1/6N9CTIg0KbD\nLNlYyABA+xDw7sSUCfQjE+jQYIMmtWLQvg/zjk77g6B0NkhUSOCsEDZxGLQLCs6bMhPgii0ttJL/\nY6llgW7HspbL25rOayV2QB2PgMwQaBzJGULNjvVyyYCAKjK/VgOIi2IDAiBkB5SCDAA1ZlDrXjzr\nVhxbCcy2R7vdja0DmQkc4yQBwVzht8ggcTaCBFX8bBrQloQWHZowbzmwIalsGDCOFggBJjho74p1\nUzTH46Air6KaOhgMysSnq6mxskeDXjUsJfN+DZnF2NF34UYDhAOBQjQPglIIVmHs8eA1nLcYp0uj\nIMAnVs1TpfH5E2tAMDYrpnIxOvsoEziUFdAyy31bUr8DqUWAXnsAi6BePuEKvnR9CQAkvwDrRiwN\nTZAchhILqLUejEwgdhZqNmfYtGfRMUiYQAYBCgbT8QQERwQoMs2WWhPGJsQwwPoBjXMwzsG4AOM9\n4AEVApT30D4VNl7BpLLrdUDQHiFN1Oq0hjMKgzEx6AgI1CSIZkFMFe1ilNMcOylTw4N2W5o3X3qo\nqdNfC8Bp+KDhvYX3zbzfAK3984IqA7tO41gWn/77WWWt2QWJQqwpj1ItTtnwg2kxAK4cEEhmQIkl\naCG+FCQQEHwDPCoHhVofAgkIRhOBdBbaxCbCraamwBwEZHCIIECBIteuUqejBj2akIBgcGh6D90H\nmB5QVBF4WaYyy1435kWwgGsA1ygMjcbQmNkAowwC3F+RGcEx7oH2atSECUzNmHE7sgClAKMQVJwm\nbVAK8Fv4zAw4EGQmsCHfmmdIkhSfNgpwf8IsMyQPJGcFkj+LmwhZJJN4DRisaymg8pCAYMkJWDIF\nlp4p2fqlOCV7jryXm31SS0GpIxFlAuJ8Ah5q68duw9kvsFGxs1BU8rOZTyADwDFOZsclILiGe5Eh\nhB02foc2dGhdYgCDgx489OBhugDVI4ZzAcE8b3SDNEuSh7FAYwNa69GbAY3p0ag4VDrOjzD5JnKr\nAWcD+xOkYOaXCCo5LpWGsxouaHQuzpvovUVwGhi0vBpzBoUW+yYCdR5K4JD7FoyFRap4JBNBAgLa\n/+AQJc731/wCNR9blIcIBLVrh4JAvk+yu2pxVoIAH3zImQB3Dq5qOQhQ22kA0abNIHBGQGDuE6D+\ngjkzmAPBEU5nPoIMBBvXoekCms4DHaC6ACQAAA254O/1syfCsy3nSROBIG4D0DiE1gPtgL7VaFuF\nnUkgoOaDnyIQnBLl3x/wBADzRs34z2LLhYYzGk4bBB8BITggDM2+WcAXVuGMoGQS8OvUNC+CwaHl\n8pBmxHz/lDvLcfblITcfSvv0XK0ppdZaQK8rYb/2s9J1Hi0X9pJLQZqUhO6PIBDG+QRi1+EObRPH\nDsy7De+3DlBGUAKCHFp0uOZOsPUd2qFD0/ewvYPZBZgdpinA6EIi1HF2HiCwgCJ5ocZvD0ALhI2D\n2ijopoNtAqz1sDpOpqrhx1YDMv3pDAT4cGc6BDrPhOSVgQ8GvtFwwQC9QugNQp+ch4OaOwnp2gl8\nwRX6fykwzFxUigHBUiUjlUve5Mdr8JLvgKKQ5MSRmhJluQQ+Apox0rUlP0AJaWtxamDAGAHdlpyF\npW7GIjMI48xCdjOgbbs4ilDoFESZgOQonFoTMkDEc7nV4Nif4mjXoekc9JmH2gUoCgB5m0MNCOb9\nieogyVlSF7e6A9QuwGwcmm1A0zqYTRpUBD+aMwpz/wAAxg2mtgRp2LODgTMGrrUIWwPXuzh9ukME\nAsoCGrLP12GkC6lws4DrdrXM8UqNXpfsLgkISnEkEKDPvTJjDWqmAM8QydFSQlqptK5lBCQK/ek1\nRlDrVMRmG44zC/WwTY/WxqHEEgBMYXIccrPgKLODcIprOIlA4Ds0GGDOOmzOepizAJwC6gxzEFgC\ngkN9BAstKHoXt6EPMM5B+fhQHTyM8Wh1dBYi9RHIJYIDQA65LUEEAm0wKAvXWAybJq6l0JsJBLLy\nU0aQWUFPvoM2KeaWQfrNi0CwVAnxDkVZiZfKen6WBBB0/8oAwRqRlJdLzS7jP4RqeOGHUZ8A9xGs\n6VDEWw9S4VONj3MMtmlmIR0b1KIvgAbKCLhjcA4MxwkQjkMMm26AVQ64N0AnEBhDBoGzFLhpQH0E\n1FdQ+i28G64EhrTPRF7p2AHGBbTDAL310BuPZhPHGmS2DWQMoiZB9h7wjtF6CmpqqRiMxdBa+I2C\n73VcSyH/E7qSEgUG+o+lJkVqLsyKo1RO76eDERVqQuScubimxNVA4L3HL//yL+OJJ57Ahz70IXzl\nK1/BJz7xCdy9exff8R3fgV/4hV+AMWb5QeeSUu3Or5dYwiEgkST/Q0oFs6NwTTfjvRox2slq4+NE\no3l2ITWfVIQOHpr7Bs7GMAOHEM2BY5xEU8CfoT3zsMZD3fVR2U8BnGAOBKeQgYA71dYAgeRApfmQ\n39HFvMgtE8ohtlq4OJgI7YCjcAoYBYRU1NWk+LRbER/oNO+UPPVdGKxF31q4wSB0NrKCDaKJkEyW\nGSvgDI+aC7wXOtXxWcHhoQYGpTIrdSTKLwpse/+yGgj+9E//FK997WtxenoKAPijP/oj/ORP/iR+\n6Id+CJ/85CfxZ3/2Z/jxH//xC0vYeimZFTzO0o8hJgH/J5bcdoijkB63Adh46DTbcNvkOQanjj4b\nrPMVHBEmcC0xgSN/im3Xod15mHsh1sB3MWcDZyRkVkCVlAIBb0aUfFEULDlI0ubTTLe3wjsGQHnA\nhOhE3YYu3pMGFoEo/mQWzIc3zydnn6ZB22CHQRv0jYXfWPjOwvcmLrXWKZm9UFAsmYAUBGhezCrp\npTK3pswuO/kuSiSOvSfPP/88vvjFL+Ltb3/7eO7v//7v8Za3vAUA8Na3vhV/9Vd/9WBSuCjcNwDI\n2syr94L9Rpcy19hnBKUORVJLwayQeaB1IxBsmJNw3t9ubhqUWhBGJhAiG9jsemxOPOxJ7BeAuwBe\nSOEuO34BwD3h3DdTeEE4/40UStf5+bvpHfwcS5e6C9iTANsHbLvo5DwOJ6PPY2oWnRhRCTAnUE3n\nTJzZ2bY99GYANi7+i1LrDg3c7OEVQfYRjMVoiYmWvI1caJl+cWQVI/jDP/xD/PRP/zROTk4AAC+8\n8AKuX78OrSOOPPnkk/jv//7vC0hOzXEoxeVOmBLylhw1LB5lAPRna+FWCRQoO2CdiqJvYIDNHWqE\nkYB0ApESK6DMYIPYN2DTDRMTuIdoBmwx7Z9gzgZOMTkLqcOQ+wioaSAxAsk04N+fzIG95+YOPbQl\nogfMaUAbPPx2gGu7aOPD7g1qmo9SiD0R4hSt3ZirNG973aC3PVxr4RuNQBmLZBpISk+dg7SoWfJd\n0Z4plzGxLBqSybx5hvsGSkJ15nCTYREI/vqv/xqveMUr8PTTT+POnTvxNSEghPnLlLpf9KI20Nq4\nFAhqtr90X+EHceCmFJAC+yEtBw2g2gDTxFWIWjXv898QAJCAYAvuPCQDkHyHthvQnHioe5iU/3+k\nbTYPKAhwE2GHfWchNQ3WOAuzstBvz85B7nvgXXTzL+wAcxKnIfNqgDMdnOHrJMyBgM6HGHOyG6c9\no5Omt6pDZxsMTYPQWIQSCygBvEQkeY/T3AtRrHCkWr5WZh3mQLCmk1F+7+FjEhaB4Etf+hK+8IUv\n4Itf/CK6rsPp6Sk+9alP4eTkBN57aK3x/PPP45WvfKV4/507d0YAAYBnnnkGN2++Tki8xAZKjhTJ\ncbjkCJR4HqNpUsFOheLmm1Eegiy1EGS/QKoZ9UbDtA0ae4xGN2hxPAIAn05sGhy0f34EDZ/HC7i4\nsvFRGpJ7hKh8/+Mm8HrsNwvyEXdZ6cdRdZCbDQuDjvZ+B68pebMiB02ap8c342DPFmitgoHG1hk8\nphp0ej6MmedSJafiPaZFv23Q2wZuo+EfC6TpVE0rMHN2JAWSlzffDOBDYK0suSDljGkQKZqEhLXA\nx04Hdp4zCCxcA5599tlx/8aNG7hx40b8nYFX7RX5h3/4B/zJn/wJPvShD+FjH/sY3vKWt+CHf/iH\n8clPfhLf/u3fjp/4iZ9Y9ZyPfOT/w61bn0tHJXsdmMOwwj5fl1y4UonjQwB54376aVrtdwlO8wb8\n6i8At/4IwvyCiAt3XkNcyTeHxxCXJb8egMc82uun2Dx2iuP2Hq7ru7im7+E67uIa7qVwF9fT9jHc\nxXW8gMfS9etkO+73p7i2O0V72sPcC9DU7r4H4Dt/FfjLW5NpkFsOODPghZ83l2W6W+pHwGtOquA5\nq/nCLccsD3P+/c9fBb5yC3gM8NcBd12hO2pwtz3CveaI5NC0vTtuH8MLuD7LLRr3nr+Gu/46Trpr\n2L1whO7uEfCCAu7qtMXku7hHwgnmecicr7/6v4Bb/xfmZpan4547Fmh3TgmlKSpLXtschwIEHXYs\nDXxKV8KHUJJz9yN497vfjY9//OP49Kc/jaeffhpve9vbzvGUmj+A1vZLzpMSo5CulQL2azZaw1G2\nIDmPJGeSBdB4qLw0uZkG28ypK63j+rEFoSUOsNFUCDG0Q+wxaHk/gazwA+aFmBZkyTRIZTKkchYG\nxH76tNVA6FmoTArpe1XGYFq+Z+P4sV9R5ef1KX02PtOagKAdNrrDYHSc34AMaZ7GMXbJIJgMgzwh\n6ji6UQ1o9ABrBvSNg2ocQmPkeoP/1xKhlNiQ4h91P4EK94XRjkj5mEu+vlzXHwQEb3jDG/CGN7wB\nAPDqV78av/7rv37I7UxKXtMFh14VDJZ8BBVZsjC4b4cXFr6ftqrxCQTiqsTzBUkny3ZOcvcnGx2B\nwMfQ9D30mZ8rP1XyIW3XAgGhvj4BgnOAy5UPMAeCtDUWMAkINDWTpFGMEqDQ/BxS+oiyaePR2B4b\nq+KiqWaCzjapfl7AZZoUvcG0uhOZgEX1sLqHMT1008A3CsEa0RQU/3GpbuJlZJ3uoVzYak6Zmi9N\nQth1PoaH2LOw9kFrUfKQ+yr+B9pkyAGB3lqySAogAAtoG4HA2AkILOZ+78lhSKcT23ciRkbQYeM6\n2N7FsQOZ5nMFHyAzBeYwDAkIAgEBNwCuBwYXQ02sARoTQcD0ExioNJJP8a7K0ngFCgRnMd8mhhFg\nNw6btkOvYt+AaSWEPCE6zac88xFb8DXnu47LqelmiIur8n9W+6e1yoGWmaDS9y6Uuwsv4xwE8nbZ\n0fgQgeDFFPqXOK8TopQoX40FiAUqQFkPYx2MdqyGGsYtn7ev1qLQuAFNF2DoAKJM7amS92SfAgVj\nDmEXg+uT8vdA74DBpxCAkhdJKcB6wDrADhEQbBODHgA1MCCgec1BgAIBcb6qDWB2AU0T0KoBvZ27\nA6VgSd6yxdhh0cNoB2MdgvHwNgBGlZldjR2UfNUzvStRzSWFf3HlJQoEHClXmBmlf8WBgCu9WHjC\nxAhMgDGOsQE5FEEgUCBwaDofhxJTRx8dP3CKSal402EKIZ0PO8B3wNABXQ90A9CFGKhbqpTLoztW\npdADoQGaIYIB9THMzGeKyznvcppJHwS9A3QLqNajsQ5NSMyJNMHm2Y2mvJuDwAwQVGQFxrgIAhU2\nVwUAanVWdZx/7FpT98WVKwoEXEO5cP4p+SEK9y/hRsmsE+lkZgQ+1kLwoDMG8+XH+Hlew9kwwAxu\n3wktdRLKSiWEcAr4sxiGHdBnAHATCOSxQbzfD8+m/MlDBg6XpgMMQOMjJo46I+UfddINmMZB0JaH\n9K2mjUBgVZpmTU3g2aEtguu06mJabUHHJtfB+pG5xf+lZIVf8h0Vi2Lt4hpzgNr4FzeuQJIrCgRA\nPRO51krXCyBSYgY106FgGigLKBPieHvt42o9pDf8vLAO+0yAsoMQgUAPPs4sRJVecv5VgAAZBE6B\nXZ+CnwAgB+705z6+GRAggUEyK5wDfLrJ5r8gKRJtcnQYfQQjCOyQTIQAPfhpxmUlM6giiCINS1Iu\nLptmXFwcxQYEq/ZZgFT7S36jpTqpVtYWzYS1foP7lysMBEDd/iqZBkLmSuDMnUD8h5cKyQgOHkgr\n+RrtyKKeA/hYuXpIBTnPNdj7/aZpqanaYQ4Wu8kfEHaRCex64GwAzjywC3stiaPjvwYEGfdmU/2F\nCAJhmG7QClNTI1U22hlLanYnTe568LA90Ng492FWbmlBFJ6Hs62K/0QbB5g08arW++x9zT/nZUb0\n3ZVQY8l8DZiV0wcoVxwIgP1MpHBO49QYArlcukUChGIBCREIrIPSLlJRlVb1JaPn5ULrMV+q3KVp\nxx1smm14nGMwKz3t9Uarcz4JyW7yCfSJCZx54CzMyUR+JJ+6UAKCnOMUOAKSg9EBKkQdsxrQloGA\nTS/L3ZHzHIL8W7roeDRdQOg9rJp8LnxBltLybRkE8srKWsd/A+uiSaAzWlX+d63OybLIDOiD6E0G\n+9OeHzp34fnligEBR1Ep1O4tPaNweq15IJkJJkDpaI9qxBV8pxUBPfi6fvPpNfxYeEda6xwMBQHa\nMU1QHPj9cyE1D/Zd9Ans3Nyy4P5HPgq51vIn9hMKiDOdD4BVQJOaBZU0IUh+If0m1t1X9YDpk/O1\nGYpMgAKCnh37EZB18FDaA9pHH0HNFKgBf7UYli6sMQXOU8bPL1cQCCq1+sHPWekjWM0EQAhJXJxT\nqTia3ggFU6q95s4uNzkJXYDOMw7TSTdLgdeuQwSCoU+tA27PctibypAOQygBQTarJVeWStesi/Gi\nImMa6beUZhby95smTL4C7E1Jws7zBdl8Ohfi8uo6IOiw3F+gdO6+fASHSDYVHozT8IoBAbC+9pfi\nSaygcFp6RIkZCKxA6cgIlIoFbmIBHmZWY5XorJ/vex/b5SkI8EFEVHH8dD6kayMQ5CZClEFgrbMw\nz94l/YHRAkitB6YHmgxmPfuWnGZpYNRAAHAAjC8zKs4KNFH8CYynBVOUClAasWPRGhOAg4BUbmiZ\nqrKBtbX8g2UDwJUEgjVSaj7MxbPABKS8Lv30BR+B0khsQFq7j86+5/cKbq69xv3g40AWac59aXgv\nXZgjxfGpw1BPmgipm0Ga1HgNEEgznQMTW7CI77KIzKBNSq2ltHvI3yfEU5BZ1X4+TnFmU6CqxNa0\nR8isoAbuXH+lSr6qr7zsPXjlPkReokAgaSuHdCY1eifhSc08UJkRTKYBBwMzA4NQrtlCAhKuJEsh\n164uDSAaYtfhgTUTSu6F0oTG0hCB0pwlFAhyH6vWRzAyTUyP4mAWUAY2EpQP0CHE2Y+T809iBBx4\n90A5mQYqT4smUX66XShCdSug9KCLMBvuXy4JEFw0MpaK5op3SMpNr9UAIVsaCkCqbZTan4M3p3Fa\n068QwrQ0eWQFkAMfrZq7A6b9kAYP5TZ+Pui1VAkvAYE0vMXUnuVjGowjIEAj82+RhuR7QIWUJ2Ne\n7U94zq/FNNJl1TMr8IAKUCp9j8QKpH9Pzx2kzxflM1h6R5Z1PoVLAAQ5Yx70888JBCXFr8YNYyHT\nii/NMS+Y0zIdBeYQHIx3UD7sD97JClIzDdJQYpcGEA1BnnagBALnAQKLPctk1uHIDhEM9tIcsP8N\nfE4OD2gfoL2LYKA4gM7zdD+vp+VRtPIJrMP0EbzWl/695C84GAgeJBjkBK5vfrwkQEC3V1hmTiNa\n8/tCgZxW78uaTSfspgV5T/lLoEA1lp7Pj0lbaUIiTjJ48yHIY3MRy59rKs8akxnYA4IQSt/Ezuc8\nk9ZIpCsiSmsnZjAYv0qFFLCv/FdOcqJzZ6R1jOBBVsUvT+FmAuYOQ+mcYj+LFuKZSNpFQZ/3/OHH\nQlRW2RaVuHa/pKtL94tp5Q6HUuKSxKwW8ik9UGZic/AY5XKY6g9NXqJA8BD/KLchR9kvrKKyp7h7\n13iVLG3l11Sbn/klqZIuiRR31f2lC+f8xolV0XMQgXaKF+aRoR4BwUtP1tGhBy4PIhmX5NNeevLy\nztiXKBA8RGjnHjT5IEWV6rIYd+8a917zc+XXVLOj5AiXnOSle2th1UuldB7wjVIezj0uIg+YR4Z6\nWWPBSxQIHuIfZQ6vuf96cnF55uLijxBBotThJUupVUNQPO67lrpDLHWtKMWl1wrdt+S08nSWEpck\nZnXJwCq5ZedtCqP48LIGgkvQasC9m1fYUJsZx1MJD6TQ1RoTQQorbQwjl/c1rXQO7Hx+TNpmJS0p\nP+8sVOpQVBq6z/VXkXfvAZTUJMe/iZ2nbTC8J8HcLSixgvhPxkwJKgX20VcSGJYcRrJcAiAAYjFb\nIqLnFaldbWV0Ptqm5JXfixsLVggaPtQaDGmPgalAT9u4xLfTZr8LLFWU0lTbaV8ZwJg40aj18x5/\nfFSw1FyoUAYCKwT+zDGomAbDhyPnoIRvEJDFawWvDZwahw9B7oUh5TXpTRA0gtcIGbRLzZj83/M4\nBxSvc9xwDlly8+7LJQCColF9we84JxDw5rlS4WBt9SEoeK8RQl7JN/dpm9dMk4mgxZDBwCsPaFeu\nvrkGEqVSSfmsmUYC0kCxxOL+gKAIAohzEtgESkqa95F/i2RraCAozUCA977Qe9dofo//IkQwiKCd\nPq7Udio1g1BAOBgIHiTduJJA8CAkmxqlXjdCJi01mtdqB6HwBK9iCPv+gGlArJ4d7wU1xQmlml8K\nWanylOApGBt79bUq9vKjkwNJn5510EAGAvKK2bpRfBW4FvGdVic2kMIMLTJ4cTQRQtAKQSl4VZve\nRe8pPv8PIajxP4mgzrcLRaiu46UHPWhQWCcvUSAowXjA1JdVuEUCUskckJR/FhSCRxEEaKGUx8qx\noDSglazwJYVhVbZu4mCfpgc2inT5RXmm4qzsvLguAUFeLW5caE7F0JiYBs1XHRbMmT1KweLxWQYo\nAJQCBwEfdGRt6Z+JbEDS1ZLCl8rQeJGWvWrkF12uIBCsyUBqYkjOE/oMJZ/mAVhkAeO+y4xgop+x\n9poCZQXLMxdqeK0QiGKMS4vRVXxpVayn83Q5MmuB1k6Ll2SfgCR09iEehwKBwT4IUDBoNdAaxPUO\nGpJ2Kc30PPnekI6DBZzO+bjPAkqAMOW9mQNDYgWz/tal/ytV6lK5AdmWLwjXSvLgQeOKAUH+E7zd\nTIpXyzj6nL1G6XV9Z0vxxg761DRQpBDuz5nDpyqdJilLW2UxGA2flGi2tJgEAg2mGpWcUwNg2jgn\nQJ56vEROFaZJR2pAkMEiTzsohdYAbWIke+nkaa5c9yk4o2KesPmJpnmd8myPGRykycuys1BFNiAN\ndFr616uchRflE3iwZsQVBIJSdb3m3tIz1PxRUq1fYwGMDcQQGYH3qRCGVBAVraFk21aaeMsZA9co\nmCbsK0kr7GtM8wGm4YZqiMqo2rjuwOAQZxtO381b9/LncAc6b7DIr6LKv0U0QVodGUjTEjZAnQf0\n2EB2MmQ20AC+UXCGKr0t5N8+O3DQcIEwAq8BrwGn5FFWa0ChWiRLF2oV1Jpw8XLFgECSbHtJDdI0\nDv2DhcfUwEBS+mKBUYDTwGAQvIX3sQnQKXkishogDDCx9tMGQ6PToqohKlTWurxMGOXnNm3p9MJp\nHgDt4uIjWz9dU2FO9+k6pjUfASUeMzBQwNYAWws0DeJqRZLdQPc5IyCOhtAArlEYGh3zQpVnJVyY\n9A0uxH8SvAEGE/9V9hHU/m/NZMiyyAxKN0l05MXzH1xxICgZ7ByqS+eFx1AgKD2mRhVHZqABB3gX\nayEXLAECWwUBcTpTY9BrA90AtnFz7xwPG0SlykBAQEClGYssxcQwtUxmxZZmKKLCmw85Dm00sLHA\npgFsC+iNAAQ8UGeDEHyj0TcG/WgWNKvycGIOKd+DgfMG3hnAZSAoFKGlf77kK9i7sKaMlgDjwckV\nBoIaVaLNh9L1yrVDKCE3B4b5NgwKwWn4wcDb1DlI8bWQ+QJd07Lf08LoDXoV4zc2IGx8nG9sh4mH\n50kGsylg0/lB/h6t0s/XiOsODLGPQV6mjAKBlFOZFVAgGE0Bk8yBDAJHKS21kIGAn0shtAre6sgE\n1JR/e/lEAs/fDALea3hnEJxCGJT474r+AskcqFbgtXK4VPM/WHOAyhUFgpy5JaehVK1L93vE0le4\nJP38IgNAAQw03KCjaWD05ABMYCAt0FUs0MrCWR+9fXRJsAwGdK2DJSAAoAPGxUfGVb9CzBG6CGpN\nxuZDlYAgOQabNpoDusUyCGSlz+bMETlHGIGzZgTEaj6x6/MJzjWcN3CDRhi0PDVTZXakRdOhWJik\nMrik5Isoc2FyRYFgSXjzYc24C1O0JWrI5/WS5vhyAIapod47BecMBm8whNpayHZkAj1nBmpaGL0x\nDn2rETYOZpfoNp+JNLv7t9g38kPKndRrT1lAm9isqDrExUgcsPFYtyx66ihkdVoW3caWCd3EtKkM\nUmfGZd0AACAASURBVFtEBT9ix3Q/p5kAgN/EMLQavYlAMK0NvZ9XMiCQ42AxeAvnDPz4nwRWwP9v\naQo1FMrNrPyVbIsXr8ZfkpcoEPAM5lW38MeWnIAZCGYKjzogOJUYgYFr9hboHgvpPBTAIIXW9Ogb\nBdUqqE2AkaYezkBwRD6XZgfx9qnU71/1U4ejtieTnbp6TlszjR8wqVUgh1kTAlX8o8J+BgLiRAwt\n4DYKfavQGbsIAplZ9RwAkPO/SYzAIDhdBwFpX/KgSmVlVrBoLfLi1PCHykMEAkrvAbkWl8LSM5eC\nEC+ElBS1/2NpVDopqAQAQoHygwZ6C9fGmmjQEQwkihsLeM8K+AQEO2zQqB6N7aBbwGwcQh/GpsFZ\nehpMjICDAAWCPB4hLUEW8mpCw8QO9rKe/DKTBxFZ1lmItghQE+CIBXrNTnFCuhY2CkNj0JkGnYp5\nwMGgm+XT3FTI60yPbMxbOGfhexv/Tck/IP3Tmrk4Kzch5ZdUbteW0ZKsvY+X9bo8RCCgNr4EApKo\nyrWaAb9ClnwA9HqpsAhgEHoN31sMQwKCMIEAXQy9Q4sWPTr0pKA3aBM47LCJMXUfF/9sgGYTYJyT\npyLOjIBnFx+uTLvzpqXIdFoHQbk027DEKNJzxhWOaXdg3pYoAcFxChQMLLmW4vmtRt80ODNb7NQG\nO2xGMMgg0KNFPwJDS0yEHJJzMZkFwxCBIPS6COCiWcArhhJzXF3Z1wqbJEuFVIq/jn2sAoKTkxP8\n3u/9Hv71X/8VSin83M/9HF7zmtfg4x//OL761a/i1a9+NT7wgQ/g+Ph4zeNYQgPkUYcZKOjHl3oT\nrkHZAhugaSjlsTTVds1fQAtUrxB6E8HAWfSmwaAzGHD6H/di7d+ixQa7FGsEAhWBoLEezcZBew/t\nwv5iIRZRoahkxacjF6ny5mXIOMPI+UJFApJSW6JkGhxjDgbH6b4EAOEI8FuFvjXobIudnkAgA0Hc\nbrAjwNkRQOgZCPS+weCaBAIG6NWUZyUzr/TPSx2PRp1bWybPU8NLiCTJelRaBQR/8Ad/gO///u/H\nBz/4QTjnsNvt8Md//Md44xvfiHe+85147rnn8JnPfAbvfve7V730/JI/mrYW8GNg3rEox6FVOdf0\ndG9Q+/lMb+EUsWZbjmv6aaAP8L3B0NsIAs3U9BXrsxY9+gQBuVBvsCOcgTYuWgyw2kFvHBCA1g3Q\neeWSrLgtgGssS6QBPQ2mpclzujm4SeVJYR8IpA5BJUawRVT+aylQINgCbqvRbS1ONy3OdFT6M2xn\nYBDPTWDQj6YBB4UGfWgiK+sMfG+ATkcgyL6VGgDw/y2B5IwNlGpuydMoSRDirK/dzyO1DvsAgNPT\nU3zpS1/Cj/3YjwEAjDE4Pj7GF77wBbz1rW8FANy8eROf//znLyhJSygmISfPoBrKLtAqSv/5/6sV\nDu5Q6gF0KhU2Dd8buM6iTyaC7OiKBXiHWsGP587MBmdNi93GYtho+GxXZ+XKQECV7RqA62yfhmts\nS8NjLJTuLd1/nb1bSluTmMARMGw1dhuLs6bFmdns5cMUtsRv0IxMYNo2o2+gHyxcbyMQ9AkIerW/\nqCz9l7wOqTGCWbEtgcAaf4BU66/Vi/PJIiP4z//8Tzz22GP4nd/5HXz5y1/Gd37nd+I973kPvvGN\nb+Dxxx8HADz++OP45je/ee5ERKG1/ZoJSnjckoJzVJXAgGQi9wFY8gipZuAht+U3mLXrh07B9RZD\n06Br2xEGKBjEvTaZAsMeC2jQ0/GIscOs9jCbvDRagEWIWdIgKlqAPHkJ7/PPlye/KEaQ+xHQ/gGU\nHWTzIAGBu6bgjhW61mJnWpxhizNscYqjGTBykKQORLHVJbQYhgautwidmve5YMvHF30/tLhIFcRY\n1KRyJlVYS2W2iDIFWQMYsiwCgfce//zP/4z3ve99eP3rX49PfepTeO655w5+0TqhH1ADA8lvUKrt\nuX+glPF6ujxgKuAcyGs0kZoEHSYF64DQ6wgELtLUuWc7A0E3AkHsEDuZBbnxi6/wa8wAsxmg7YAN\nPMYVlhpE5crKSsf101GJHaKS8n4IEvvJ2cgHG9CwNyMJ5iYC7ztAfQUt4IxCd01jZyx2ySQ4xdEI\nBBJLyr4C6ifYY1zJP+A6G9lA/l7eB6PUZMj1mQIBjTOaBmvKIo9HkZc+58GCALACCJ544gk8+eST\neP3rXw8A+MEf/EE899xzePzxx/H1r3993L7iFa8Q779z5w7u3LkzHj/zzDO4efN1JAa35/m5paCF\nLZ/Hi25rk/yl2hRqrjgNcPMHMBvjP9tKIY+aGzvHGKDV0K2CRQPrjtGqV6DR1CjYbxFvZ6SXNo6R\n+3QPrQaE6w5u66AeA5R9K9R3YSrcpdqOKzy1e5fKoZT9pQlGJIdi2gYLhFYBR28FYKGNQassFBo0\naHGNffXUYjCv/alpMMbxDfrQYlANhtbCP2ZjF8jHMK0Fz9kB7649CNu0f/MHAfxyOkZIeZUzAqTQ\n8N5djm1L7KFkIiw5GGVgePbZZ8f9Gzdu4MaNGwBWAMHjjz+OJ598Ev/+7/+Ob/u2b8Pf/d3f4amn\nnsJTTz2F27dv413vehdu376NN73pTeL99GVZbt/+37h163Mko2gTojTIlZey2qR9FnKJo4H1XR0D\nyQ7W1x3/J3Dr9yF3huE1G7fJH8vHCvp6QHO9x+b4FMftCa7pe7iGKRzhBNdwgmOcjOeu4y6OcYJj\n3Evb03E/xz1WJzhWpzjSp2idR4sPA3dvxZGJpwBO0naXwlkKnB5LzWkloWyDKj0HRD52gDYRbqNf\noDca0L+Kb4T/GyfqaPa15CtxQnLoLq6POcfj38M13MV1nLhj3OuO0Z0A/V0Df1cB9xDDCwDuYjq+\nl/LpXsorKZyR/R2ADwC3fi2dH8Vjf6H5HSaE4bak1HRR6rzA0VryP4Bdj/KRj/wfeOaZZ8RfuarV\n4L3vfS9+67d+C8Mw4Fu/9Vvx8z//8/De42Mf+xg++9nP4lWvehU++MEPrnnUOSV/XP4o3p/gUOdg\nKYMDRkCSnIP031myzf4AGrKfIJcBq2Ltt9MYGgttW3RmgLWTL8CqARp8bGIcnUAnOB+X9UaYVvLN\nvgAV4DcDtNbAdQ1j/dRpiHb0ydhHKTJtNaCBlq2c/XlbMg0kIEjvDqSJ0G01hq1G11pYZUcQOFHH\no0lwOvoJtuMxNxXOqJkQSMejocXQtRh2FmGnowM3gyHvlcnNgxJA8r4F1A11UHkr1fachvGyfH+O\nQUlWAcHTTz+N3/iN39g7/+EPf/hCE1MW/uHSYKPsPKw5YGiGln5OYh/UD2DSbbSACB1xxuvcWZib\n5zogtBq+azA0Hn3TomsGNKrHTm3GuXP4INqs9KWJu3MOZTLl2g6tMvDXLDamhzFhSmtW/gwIlB5L\nY48pQ6XCLTHeJEnNIj7/QGJTfqvQbS12m+gPOFLNWLNPfoEY+P7pnr9gO4YzbNGF6CDsXQQC3zWx\nAxGvoClwl0BAciTmMMubpZap2jUej4vkTLw4uQRjDSQlp9c8ObdkH0kIK9ldtR+ipldxrKDAUGsx\nsJizgswMOgA7hWANnG0wtAO6toXVA4whLQGJFUyTbM0BgK+uHBBn9c37zhpcg0W3bQEFNNpBGw/V\nBKgW0IcAwT7DjEIttiUgSO/yLRA2QNio2GOwNbGfQBNbBwzaRPMnxT8VwtkYtmUHot+g8y2GvoHb\nWYSdAXZqn7HXWg5KrF1i5GOhKbGBWrlcKsMlX4DEDGrXynJJgIDUxHvXQuGaZ+ekezlLqHlsKSMQ\nLmdg4B1PaO2RFYE3IVJmkMb7BmswtBbaxuZEY8QpS5HnOubzHU9fyRbtSHc/jgYn+gh+o9GaHk3T\nw7YOZhOgd5iYQYdpCDNvNeBAQE0DCQhKpkECBL8BXKswtAZ906CzDc50pPVn2GKbgCDT/rnyb0lT\n4n5z4l4rgmvRdU0yCRII7NTcJOBsgIdaD0Our2KhkSKV4lA5JI4kknlRl4c86IjX9NKYA1W5RjOC\nNiOCXNeYZ8wa+hZINDUHgswILPbBIAMBNw1m/so4djfYANc06KyH1h7KemidQEBNQMCX7thjAiMA\nZM9BnLqrR4N75hiDMdiYHbY2zxMQoFoPbAC1C7FbMSn8s67FEpkCqkAQaGtAAoOwUUCLOJS4VdiZ\nFjuzmXUbPsURHkOTXKFzIDjDdjx/WmMDIYGK32A3bNDvNnBnTfQNnOnJN1BqKVgyAyT/XS4rszK3\n5AvgzJQ7YaRaiMseCgnX6HFdHhIQ1BJWMhPWPFOg+NU4pZ9FonJnIQeBDAC8uYyaCbxBwwAwCsFa\neAP0ykNpD2MdrHHQii6AFkYWwEGA8wW6TkKXatdxFmRtsWk6NHpAYx1M66C3Hqb30H2A6gHN5jcU\ngSBnq+QjSN/oEwj4RsE1Os7OZE2cT8BY7FQ7G0CUFbonQBB9AEczUKAgQe/L/oKzsMHOJRA4a+FP\nW4QzC5ypOQhwZyEFg9KwbqkT2V5nq6VKpqbUS3FqUgMFGqcsDxEIeC0vXZdMgtozqV0mORR5hpfa\ncFPacvQBc1DIgECdhlTReasBv24AWI1gNZzWUNojmBDZgPZQIQGBmlSbrvc7ZwH7i4BmILiL68jT\nnPUm9ldsbYc2dLBhQBN62B6wvYfpAwL3EUhmQRap/4BF7BNAJxptdBxgpew4wUqJzk9AsJ35CMpm\nwmYOAmGL3bDBbreBO9vAnbZzJnCGfSCQOhWVHIgl86BYvqQyxs3TIMQ7BAhqJsL6OJfAR3CI8A+S\nhiXz0ltD56zlUt+EFLJJwBkBryW4aZD3uSON92FSCl5bQG/QBQ+tApQNcYivwmgSzL+Qeg2mRsXs\nH8gdakYgIB1xcgclq1KTpXGwykUfReNhfAB8gPKADh6KLhdOTYO0DTouPRY0AK3i4iMmTTmuDQY1\nTS9GuwDLvQMji5n7ASQfQT5O1/0RzvwRzoYturMt3NkG/tQCp2pq+5eYAAcEqblwiQ0ArCzxsNZU\nKPkBzsMWDmUUVw4IgDkYSLU+jbMUKHPgIWmxJ4/kjCAH3qQota1zZkD6Rnlt4bWZQCA1A8ZN2GMD\n00o9cy9CnrE3K342DTgItCoxAtXHfgxhgG3i1sBHAECACQ7Gu+rfyNO0B6hxNadx8RE2USsfD0AB\n4QzbEQimbsVz82BuDhCGEI5w6o6w67bod1sMp9syCHAmUOpTICk+/f+zsQUlEKh1ClprNtyPybBe\nLhEQZOWVuhxz+0exezTm6KmwHgy4IyAfp2dSp2GPcsdGCgglZkDjzmxslWrTBp2e+gwoNQXedjCZ\nAXkR1fm0XBMjsHkmgxEM8rwGcRDTAKvyhN95es8IBnEgkxuBiPon8jYv1OrHhVvmqzQ5zGdgol2B\n4yjL7cw0uIfrqRVhQ2r97cwcGJsVwxHOwha7YYtut0F3Fp2DONUTAJyRsOQjWOpHkIMH4pLXuTzy\nMsTL01oACNhnCtzpx+kZv3Y+sLhkQJD9AkC5P0HJ7s9C71v7I7g9xzI0nxogEwjqQJT8BrxnNMU6\nowAdWYDXBkG3Ufl1iOcN4sq/s25FHASm9Q+o0k2MIE5zsjdGAXk047RQ2DSyMbVaqPnyoTHHp/cH\n6HFVYrrAyDD2hjB7g4Cm6cXmIwkjeF2bD7dOnYXmjsHMBLY4c9Ev0O02GE5bhFMzZwK0S3DJRJCY\ngAQAuffljA3kMpjDIQAgAcESAygpPAWPw+UhNx+WannerJj3DWShmUDZwXlYQQ6GbNX0jxXmzICb\nBtKYpooLIuJaqmG1RtAWQwYCIDU1qjhnip47Cuer+MiMoEODDWUCDAyo4lNGYJQD7dJcBoLJLMnw\nkWElp3F/NODECDrBRyDNv7DLjsGwTUzgCLshgcDZBsNZm/wCejILsvKXGEGJFSx1M56ZBHnLR3Gd\nlw0IFdGeUGXnjGFpX5ZLMnlprfVgKU7tnqUhytwU4I7D/E4DBBWjAWVn4Rql581u9LO0ArSGVxY9\nEBdR3USFG/FI6VHx+Bp/uS/iAIMODe7ieoKA8gjGwtrLMHBjuwQ1BqKo8ewcCCZ2QtNVGznYjYDQ\noofFCY5nTICDQuwnEH0C3S4xgbMW/sTGFgIKAhQIpFaDNSaC5BsItEyB3bAWDKhynscHsMYUWG8u\nPGTTgNb+a+JwG4oH6huQ/AZrwCD/ccrhib8gr5xLHYYl3wFXeMUeyfFNK0BFJXMhLdkdYvdh1UQP\nfchLrCtJhamzsE1AMAEAH60/X0u4BAQ1qjlxhv0FXM0MCDJLmYPAfFqxHi3uJSDYcSAIm7GJ8GzY\nYtdvIxM4beHPEhM40fJoQar0EjsodSySGgDGRR4oRSyN6V7DBiR/wFI5B4m3JOvA5RL5CNbIITZQ\nzmw6VO5Qf4Ggybz1oAQAtMedYo/ibGCqaNOxArxG8BbOK3ReI2wUXFBwVmOw+x2RJxCwo9rfxTW0\nIyeIU540o+swxl7LCGQ5DAi4adAJMHWCa8hTtVEgoJ2FurPYOuDOmuQTIEwgD7emQ4YlJkCHYK8Z\ndzCrsKkpmSsP7md6UOHBtBxcIiDY0wZ2nM+tzYisyIcAAf+hGe2zbyKxDh+AQe3X8EugwM/Tz5k1\nhCjAKwSv4mIc3sIHhSE55Zw28JquBJxXB84UvBs98NEsaNL8Ry3yagrrGcH9AwFtQqSLuNDZhidG\ncG0yGcJ2HF58Fjaks9AWw+kmAQDK5kAJBEpsgPcwpGMOfP45tAYfENuGaWvBGv+TdO0QBkGl5FAs\nXZPlEgABdxhSas9BAZgDw5pnZ1aw1nlIzQJqKqSsCmoeLW971AFB8gms+aQEDN43gFfonULwqVei\niaCQFwXlU5/dwzU0IwDMpvJcBIKph0Le7idrGgUxNWuuBQI+BxPt+xBBIM0r4OMAotxteNZZiCq8\nNHnImRBKys8BgJr7AzAtWuLpSUwUsdZnoETxz+sXAOb3cSfj4azhkgBBtu+l/gGqEGftc6UMW2IF\nWbtzy4GePyfnOW1OlGz/EjvgwjtIKrLjQwSC0MB7i+A1hqDhNxpuk1YGVg3iCgjRW28xjK0GmXyT\nNX8WgGDeV5GOd6Ay78tQBwMOBHRaVt6iEJ2F2+hADC0616LvGnS7Fv40dRumILDDMhPgfoHS+ILS\noKNZL8IgXFgCAhqonEdxS2WanzsMDC4BEJRk6UOWHCrcSUi1sOYT4EwA5Bl+up5bEehj1yh97VM4\nDnoATpFkqggGDugGDT9YDI3FYC0Gk9ZMMA3y7Mj3cA09mpni2woQTCbBHAT2J0HZv5LBQDIR+DLw\nFJLmcw62uOuuofMbdEOaVKSPQ4ndWZMGEOlybc+ZwA7Y61OwtvmQNhcG+lMoTRjYuZoJUPMh1NjC\nGrC4f7/BJQaCmpQyR8oQCgRZpJ9DjfXcOYDfk8+R7sc9u/UQAKDJzvdxC4Ydh0EjDA1Cb+A6h2Fj\nMbQWfRtBgQLBCY5Hs6AEBlMD5Hy+A4Opa3GNEWThT6BzLVEQmLODuSnTocU9dw1dn6YX6xq4XYOw\nM2kosZorNK/9JRaw1HRY6liUOw/NHISlsQTUR7A0xmDJN0CHNPKy+eDkkgMBrdUBmUNzUJCQlQMB\nrfV5R6Zs+EuOxmwm6HicV0Zyar8Fgb5qjV+Aukd4ZUHLTVq4MwwqgYKO6ysOBn5j0YcGrerQmdhq\n0KhhGmCk9hdjz30Ap5kMppD7DvDxDvRs3p8DADURhCXgA1mGLFgMvonTiukGZ2fHGHaRBfiuIZOK\n6H1lzkAgKfoSCNCBR6WRhp6WqdlPELZS82Gt2/EaRsv9AdL2YkDikgMBoeJlQ3qF5B9Jn5ufnWE/\n9xLiTsrMELhTgNgA1EykNTtQZwj8H0osgJugY1mM4BMGA9cp+D6u4tP3cchxd9zgnr8Oa4dx4dT5\nQuFTR+Cp/WGq1yWTYD/5++YB7aA8mQfT2INxhYa8FuGQVn8aGvRtg91dDZ8mGg29ns8sVFL4Q85L\nA4y4abDnEyjV9lmxpY5ESz6DQxVYAofzPEeWSwYEkqJLBnS+ztFTOsdBAJg7brjGUsM/H1NUz8CQ\nq309KatS+0Si1FLAP5ubAxKLpGVxUDH0QGgNQlpfEb2FayyGxuBkOIb1A6wZYHUKiRkY+HEmJAMH\nraizcD43YvyUfUaQt9RxOAJAIN2fQ+p6HBII+LQqsWswpKXgXJ+GPd21wM7E2Yb5cOFaWGomLIGA\nOPQ4ML/AXuaTcwHzH8R/WM08qDGA0jleaOgx2Ln1comA4KJq+zXCPbi1Pga0qSg7DykQAHEwvpq6\nIK9xXUifIPgE9pJBQ49pTsAeyCv4+EbBH2n0947gGoehGWDsEFdF0nEBVZOCVnFqNGoacCCYWMEE\nmpKjcM4KdASDYFJfCIMh9Ylwblqa3PdxUdLQKcDquM4AnWVYAoMSQPCWgdJ9/Pl8QFEAYg/CUsbz\nVoMlf8B5w/2yhvVyiYAAWAYDzgbyuUOAI9/rhHMlDUw+AREI0ruDSf8uRN/BIf0FlkBAYp8DpmnJ\ne8TVezoFtBqhAcLjwHBXA42Hahx0G5dFMzZNQmI9tE3zI+o8P+Kk0vuAMAfbPIGaOFtSSGDgEzMY\nDNyg4ZyBGwz8YOG7vDS5RlyZGHERmLvpe5bmDqBxah2FSiCQfQN7JkFmA5yO3Q8QZGZwKGiUfAOS\nnB8EgEsHBCWhTsO1vgJ6j2SMcw5PHYeSeMTSAuwxgtyKEEjUmceZvIYqPH10qZzkMthiXiZ77M+S\nnJdZyxOhvKCARiE0Br5RgDUI1sObgMF6KOPjNOd6iBOoprkP4vDjtK9iwmTTQMGHqPghJDBI4yO8\nNwjewDuN4KJT07vo4PTJwTlbjbgD8DgmRiDZ8VJtLtX6VMmlZkLej2DUUw4CfAhiqdVgzSQkEk1c\nU/PXzIaLkysEBAGH9zbMma0L53mrAPcbUMk/nPsI8v2pA1IgUSWLRfr39Bzv5czLGC2bdKbkDAJ5\nu0NUqkaP06c7C8CEcY4EZQNgHGAdlPZQOsRtGgYdwYF9RMbVtA1Bw3sdR0qG1OvRxy0GAziDMGBq\nWclb+h1UQV8QzvEBQUu1PL1nh30GwJ+990+oh7YEABIQHErzlxRaUvrzmMLLcgknL6XV6lKjPHci\nUm7NG+bpNY15hnIgAOQfSbVTahoImIFB9hv02Bep81DA/qt4K1U2BRq2L23vqmkq9XHeRDWCQTBq\nmlHZBEAHKBOiz1MHeBUA5eXfMGZpYgFexQrVKcQRmgpwOgVExyYHNlrZ5nAPZQCgzGDHjiUn4JrW\nAQ9M3Yep44+DQMn5t4bu07In7UuBZnRNSvdJccpyCYEgX8+y1v7PPzEp4+xeWvtLGXIIEFBmwN/f\nYGQMnBmU/m3NLyABgQQCEiB0iEpFQWBvJmUAWsfhz3ldAgOENGNSXDwpTISHyvhNEQDiNyjmMFdT\nHrgwNnmK35QV9R7KAHDIlp+Tzu9V1jmxNTPgECDgPgR6LBWCDDSHyBpTYZlFXGLTQAIK/sFKOAdy\nTNkBrf2l3jwlIHDk+oBlyc/W5J+rOkEplSsJCLLi0/0SINxFAQQUm0JtAgLaRSLQ7KLZJlVCEoCV\nTJwaGJygPihI2l+6Ls0xkJsIRyZAQYADgQQMtPmwxgRK5yRWkCVUtiUTY0kuJSM4r1DF1sI5ruDZ\nDKAIzI+BOhDkTkcSENQYTdK6XNCcmi7xslCrWEod2GiZpCsyZ1Ngh2hvLzKCSuAAwElVDdBoKKWf\nfwdnBNx04MrNfXk1IJCc/WMRWOsP4A9ZaxpI3YbpvVRKVN8L5y5OLuGchVI8GoejJITrPAOBCQBo\nXO4sdOw+2jGAX18S0psoKIx2aB7GLFUKUvnJist7F9IyydcczLMm3cW+4tNtaWo1xY5rsoYlS8ym\nxApOMLfhS0BAdXaJPfTkPXtMIJAXcTCoMYFSC0HpPEd9biIEYb92vCQlW1SWSz5nIb1O4/B7FTmX\nRTJs808H5sCQgYC+Y9bPNO1LbYIlIc5DaMw6HXHyQc1DDgJZuUu+AokNGERFOIEMAHThlZLy03M1\nWWLAVDdqpgEHgkHY8vEAJUDgbCE3EY76xBMngcASE6AfxrelzChVJBJQAHMAkMzfNb6BdSziEjAC\nPo5A8gtAiCPFp2CQWwa4YUv9BVkkIKDvzvuOxJe+RzqXzQQgTUOc9tUcv3h5oUxAYgZ5aTUKFj3m\nay+WnIXS9Oo1VlCTGiPgxyVWQDtInaBuqneYg6EUR9LnvQFEnt30/7d3dbFRVV37OZ36gYABplhD\nJKQV4k19uVBE1EQKaEwIF95AookJl1oFf2I+4pUvyRejF0JJCIheiNEb4UISuDBe8JMoIdpYEqwh\nQIJEYhRoQaWA/Zn9XZTVWbNmrb33mWk7M+U8yYSZc9bZZ589ez3rWWvvDqGaAO8sfxgnjocIwXJo\nSQLyPFA+H0P1Ai310FEHNYIYZYBIG61dgm/TP++DBG020sK4ZmuBVjLYHyrRPJI1A019SidqZq8R\ncYz+ivoWipFfpgjS+bXPMcNt1Qm0z1pKwJ+N+uxz6GH4iUDalAg7OcBp6gKSBHieLx0/JlJL+1jE\nRPh4JUCoEyLgysCyIcQSAV2Xlgx850bFMcuWINUK6wf9sAnNhWaUBpcRjEV5TgT0g0my8CfJgWoE\nN1EkAi01CJFBGsQoA0kC/PljiMBaaeCOL+1UEqCTsUpgBKVfhEwRtMhvoRoSoOtjiCBOCRDqgAiq\nAX9gXvjjaYC0q+ZeMsejpUXLnn/pzewz0+b8NxALSfFnEnIonS+cCMjp6V+eOpCTa0SgpQY+JZBW\nEYSUgSQBGWCHUSQC7pdWSqERwfi93R1/kR2QRQnZcKgmIDuuFUhiXlb+L1OYtETB24hHgxMBUBww\nXmsgEtA2FFVzH9Ly2jkJOXFksk9Dnyt+38Qr3Ol5xGwW53KiWR7tiQjk6oBWI+CEwIct9k+oC9nt\nCwAAFGlJREFUC+y9VvOQRGD5FSkC6ZdWtJeFwXG/cewrkVLE5+wxNQFZGKyGCPggarl9JSRQGaKI\n4PDhwzh69CiSJMHixYvR1dWFgYEB7Ny5Ezdu3EB7ezs2b96MXM76L8liwNnRF4o06c3JwLLlnytR\nCLxGYNUSOKhYKcG95R6Mp0WO7zNISucGiQipBnwrAhRdiQgSlCqD0CpBjCrQgprlGzztGRWfiV+p\nz9rKgnZsWJx38vu1ChMyBZD5iSVZJAmkIQLeN1kAlJ81ogD0+cRtKieOIBEMDAzgm2++QXd3N5qb\nm7Fjxw5899136O3txfr16/Hkk0/i008/xZEjR/Dcc89V3JHiAMTMPGtpsCDOyc+ynbT9o9RAKoNE\nOcY3LmlfNKUK3Ivv9JVuRZdpKsCS/BYRkKPHLhnKTUS+YeGPZZEAVwGSGGjopCLQFAMP7LI9QDSu\nXeDL/610QFMCaYlAg3R4Pl9Cthq0VCMOUYqgUCjg9u3buPfeezE0NIR8Po++vj688cYbAIBVq1bh\nwIEDVRIBUDqrYmcgUKoMtCVAyb78XKwy0Gy1YiDX1VrFjZSLlieycEzFRAdgNCktFBIRSLnPZT85\nlfrfsMMmg4kmAh8xSEcfxtiPi3D/5ddJcnCOfSWcNaVTD6PUkX2FwFglEEsE2pzzOWxaJaC1mx5B\nIsjn81i/fj26urowY8YMLFu2DO3t7Zg9ezaamsYmbktLC65du1ZRByqDRgI+cOcDyvcuxJCBRgQl\nZWmUKgNJBHQ9eab0IDrHbZKiqKFukirgzkvHRlBUCqMYc6pYBTAVxUKfQiD/vI1SpVBQbMvUs8w7\nRkUjmuSXSkE6ulbQsBw9hgS0gYkBzbnKZX8MgkQwODiInp4e7N69G7NmzcL27dvR29tbZpck+ozp\n6+tDX1/f+OeNGzeis7MNQKfnrmn2C0hb7bM8p7WveUDx1dl5v2LXJN5rnqQd17b1yX458V4+XlLa\nBR7Fm4DOxwF0GY/je1xtSHzQgpBGDlpKLMijcwWALXfsyhzeeQIeV2M8B7JyEslYFpPxz3rHOztb\nAfzH88DWoMi+wzgXSwBxtvv37x9/39HRgY6ODgARRHD69Gm0trZizpw5AIAVK1bg7NmzGBwcRKFQ\nQFNTE/r7+zF//nz1en4zwrFjv2LbtmOeu0pH80FGXt/s9oU96bjytQzbtv2iHOeJunYdX9Mje75y\nkFNe/BrZJ0+XE3bLd4FtHymX+xSAHG4ru+GQKbDmN74AyoP5/wLb/g+6L5bMc61BX9SXyw6yJqBF\nc5kWWK//YNu2XqVPHsYrGzCLJKStBjngNv77305s3LhRPRckggULFuDcuXMYGhrCPffcg9OnT2PJ\nkiXo6OjAyZMn8dRTT+H48eNYvnx5qKkU4OEjVhnwa+k6i0i4TU4c87Xr+1J4OiAcF2Cf5RdNjk/t\n84nHy/20HTCxu8pTBUoNQqkAYTKIQNr4lDT56r8IzH3u+Hz5IVT4S0sEvnQg9EC+wbCep1LpbymQ\ndAgSwdKlS7Fy5Ups3boVuVwObW1tePbZZ/Hoo4+iu7sbX331Fdra2rBmzZqqOlIO7ni+yhXZSRsn\nziWGLT/PbXz9SQNaaeBEQG3JXUNkw4mAPLuAEjLQVI8DUEiK830IumKwshFWq6y4RgCUprWW4jbV\ntyaN5cva7SeJwKobxDh6DAloDyP7DeW4NXCWrQS3qYZEiohaNdiwYQM2bNhQcqy1tRXvv/9+1R3w\ngzuuFZpowLToz69NFFtNPVhfQKVEQNdKJ5dewKuAo+xzTlznqfpJpyMi0LIeAicFCNs0kOo0Rhl4\nU2It2so0gEt4a4lPrkXGOHesnewjfxD58HL++Jw4Zq7JAa8ODbCzULIqYCsDKDaWMtCu19SDPC+v\nkS8tPaDIT++lx3CS0BQBVw5WDYMRoUsw9peODuMblZI7x7kgouMy8ldDBNrQyGPj23+B0nEghWbl\nDzzaS8e3HD20DyCts2vnrCg/mUpg4kgAaAgiACZfGfhsZB8kQs5J6UDC3pPEpxxX2vNJnIYIuFrg\nSOzuaysF1UDOTYtzx9/wyNiE0h8TjCWCWAeX18SSAALnfTKovpUAoUGIACg6U4wdh6UMNFtuxz/z\nqGDdM/TihUJ+f8uhudPzyRsiAk4IBfYeRXXA++DE5wmDjxGk49CrGWP5TCwRpHHo0CoAkbI8TvCR\nhBWpQ5+1a2MwsSQANBQRxMKXQkgloNk6FCMxt4lh6xgkKN90FPOiflnLlFo6MowSIvCuFcZWB0Ow\nJntBOScdL0GYCCqV+CF7S/4TYpSAbxxCSmDio3waNBgRyMGyJq02oNKptWKhPMdtQkSgfZHWF8wJ\nKfZF0Z3vL6Z+a+TwPxhzKkkEstionasGcsJzB/Od0xSB3Eo4kS9ODM5zT4KlBug5YlIEnxLwkUAl\nqiEdGpAIhNz12hI0ZUARSKYJScDGIoMm5Tw5MO83j9hpiECT/uS8I0p7tH6oOTu/XlsykMc4rHG1\nIr4kUh8RzECRCLhUl9dX6/iyTdk3/pk/X4gA4PlcrRKYXNXQYEQApCcDp9hKJRBaWgxtOvJNBIJc\nv0urCDQioHZ527QJaRRju3NyKH12IgzZL59NDMjZCFpU1Gy4g85CcUeRJb9jnV7aa0Qg+8VJQf4d\niSQGwO6jbwzkuRj42pkYNCARAKWsHbvrxbKV5+QXJGsHSsFtvDAHlCoD/sX5HEtTDCElIHN9TjBE\nBEMokhiEHe+XJAZNGfj6Ll+EUFTVIr4sAmrtx5ABt7XqA9oz8HNWeiP7rtlbEbxSFTB5JAA0LBEA\nReedLGVAkPI+EedkHYEjzSqHVAzWi99b1gqaWHv057cjKCUsrR1tIwEUWw2a49B7SaqaM3Ebiwgs\nQgkRQciW91WqCe1ciAjkNfWvBAgNTARAuTIA/M5n2foUBkVqSwJy5+OqgghG65PG9Fph0kcEGnGQ\nEiCF4isWcmfXNhNwxeGDjNryOQvivTaOkry0qK8pDYswJBGQc8c6tXYv+YyhaC/JxLpGQlMVk48G\nJwJgYpRBqB1tcmj34lV9qz3HbGVE99nEEAEdJyIYEbY+QoC4nrfnA3c4n3NYjsc/ExFQe2mIgLcj\nyYFHeSnzJWmEVAyUz/xZG0sJEKYBEQDpZbllG1IY8rqCsKEozo8T8Vj3IuVQQHH7sWzPqg9I5UCg\nPnAikGSjkYM8lwZy4lpOT7aWNB8RNjI1iInsmgLQiCCkUCwVI5+zcZUAYZoQAZBeGRDSKgPNEbnj\nWDbyfpwQ+HuuEIggZPRPjHMESQRQbKgP1rPG1Dc4rLRAm9SWA1pEIFOmkLSX11lE4HM+iwAqUQKx\njj31SoAwjYgAKI2QBGtCa4Mto3WC8okA5TivE2j30IjHUhyy+k829FzW8h+/hhMBPyajPt1P/vp0\ngvLnr+QcVevlOZ/cp9TAUhJ8BQDCVqYKWjtSYdAxzemllPcRgU8J+Bzbp5ymDtOMCIBSMqhGGciJ\noMlBCd4G/aGQZeMrbnKlIdWGfDYr/+dO5VNKFolNBDgRcPjShRHDxor2lmKw7mXZaHsgrJxeIxHt\nGetfCRCmIREA5WTAo6tmS5GRwCv40laDJf2te8rIbIHLeR5NtWVEjQisYqG8R70TgRM2vJ1R9t5n\no91L608CvX/8nKYepE2oMFgfSoAwTYkAKI0QMfkul/3Wee6EUtLLAqN1XymRLRugtEDGz3G1wNMY\nfp5kNLfTMFlEEIqqViTnjm1Ff60N656xNgSrrkHnQk6bxqnTqIbJxTQmAiB9AdEFbH1phySSUPrg\nSw1ibLgS0NII+kGO0LPXigg0Gx69fY4rrw85echGtmlJ/ZCTxyiBNO1NHaY5EQDlyiCGEDSpb52X\ndjLKc2KQqkFrXx6zVIhmy0lBk8TyOrnyMZHwRXIOHoFJEciob0VwTb7HEIEkGqvPWj9lH/h8iHHq\nmDGZetwFRABUpgwIVvSXZKEpAe7sUsJD2PiO+YqfvrzfinyJeFntVAPpeLHKYNRzTh6TjmTZykIg\nf+9TAJrjazaxKoD3sX5IALhriAAoVwZA3OSXkyFRjsvPlg0/FlMjkNfJ3Ye8P7JmIR1AQltOtO6d\nBlakjSECLbpqjhNKFXw1At9xSzXI6+A5ryGm3driLiICIJ0yIGgTW7Phbfuut9QDR0zfOJFYm5+o\nRmD1WbaXZlws+KK1BlkQ5ERgFe7SEIE8H4ryvmIht7Gut1CfSoBwlxEBUPxCNAcJLTH68m0fYWgp\ngbQlx9b6wc9Z95bPw/PtWGKJtfUhdsJreT4nAl+F3nJWHxHI79An9WNsfLD6VZ8kANyVRACUpwhA\n3N56OQlk8VFrV7Ph/0Kct6635LWPvGgdX9YDNPiWGNMi5CwO5U5Hx2OIwJLn0olDqsR3ve9YDLT0\npH5xlxIBUPrlSCfwOZeEjKTyS+ebmng71sqC71pfGqFNYPm/LE10QbBSWJGb9zk2ivpsQmmARa4h\ncrBQ/7UAC3cxEXD4cvyYawmW/LdWGLQ2uJ2mMLR2QhLWSjlqBV8dQSM9y6l8jh4j62NUS1rE1Bjq\nDxkRjINPwDSFRLIP1RcIWlHOl3JYkyqB/7cUOQFN1hJhpfARgaYSYmsOIceuZNdfGjSeEiBkRFCC\nSp1HmwAWOZCdtmnJ2nwEw9a3EWg6EYH2r7TRIn2sxI+pJ/jAv9PGIwEgIwIFlaYJPscO2VqblrRJ\nxesGoQjIawS+/kw1YmoEvuvS3CPGMauN5I2ZDnBkRKBCFn348TSbkGLIgSastec/tOrAi5XyGKFe\nawT8s3Yc8BdLfc4Xs+LACbWSgiDvQ+OSAJARgQfaZE1bP5CIkfGavUYE8nO1OXStYTlUqCAYU0gM\n3auSNMAKFo2JjAi8COWdhDTLjb57aWpBUwqyFuFrs5K+SEzE84Xgi/q+a3y7CK3jsc7rq0c0vvNz\nZESQCqHNQj6EiKQStRBaqUjzxzAaYp5vopwiJoLHXhNy1koUQKgvjY2MCFJBTgK54SfGaTRY+wF8\nRcFQMdPKt9MitOOwEscIObA8FvM7AGmOhyAdv1pCrX9kRFAVaJKkUQZWOxIxpJJm+bDS/oXuUY2z\nWU5fyT0mKg2S951+0V9DRgRVgSZKtdt35YSLddyQ/OVEUEkfKymmxbYbu48gTZsTXbOY/kqAkBHB\nhECLztVEYV+0r2Rz0ETVDCYK1aQTvgLeRPXn7lEChIwIJgRaRJ9olUCotN16IYFqMFlkFlOHmN5I\n+xc2GaIwmRPq7p2s2bhOHhLn3N09AhkyZKiNIti/f38tblsVGq3PjdZfIOtzLZGlBhkyZMiIIEOG\nDDUigo6Ojlrctio0Wp8brb9A1udaIisWZsiQIUsNMmTIkBFBhgwZMMU7C0+dOoV9+/bBOYfVq1fj\nhRdemMrbR6G/vx+7du3C9evX0dTUhLVr12LdunW4ceMGuru7ceXKFbS2tuKtt97CrFmzat3dcRQK\nBbz77rvI5/PYunUrLl++jJ07d+LGjRtob2/H5s2bkcvlwg1NEW7evImPP/4Yv/32G5IkwauvvoqF\nCxfW9RgfPnwYR48eRZIkWLx4Mbq6ujAwMFDX4xwNN0UYHR11r7/+urt8+bIbHh5277zzjrt06dJU\n3T4a165dcxcuXHDOOXfr1i23ZcsWd+nSJffFF1+4gwcPOuec+/rrr92XX35Zw16W49ChQ27nzp3u\ngw8+cM45t337dnfixAnnnHOffPKJ+/bbb2vZvTLs2rXLHTlyxDnn3MjIiBscHKzrMe7v73evvfaa\nGx4eds6Nje/Ro0frfpxjMWWpwfnz57Fw4ULcf//9aG5uxtNPP40ff/xxqm4fjXnz5qGtrQ0AMHPm\nTDz44IPo7+9HT08PVq1aBQDo7Oysq7739/ejt7cXa9euHT/2888/44knngAArFq1Cj/88EOtuleG\nW7du4cyZM1i9ejUAIJfLYdasWXU9xsCY6rp9+zZGR0cxNDSEfD6Pvr6+uh3nNJiy1GBgYAAtLS3j\nn/P5PM6fPz9Vt68Ily9fxsWLF/Hwww/jr7/+wrx58wCMkcXff/9d494V8fnnn+Pll1/GzZs3AQD/\n/PMP5syZg6amMZ5vaWnBtWvXatnFEvz555+47777sHv3bly8eBEPPfQQNm3aVNdjnM/nsX79enR1\ndWHGjBlYtmwZ2tvbMXv27Lod5zSoabEwSerlV3XLcfv2bWzfvh2bNm3CzJkza90dEz/99BPmzp2L\ntrY2uDsrwc658feEehrrQqGACxcu4Pnnn8eHH36IGTNm4ODBg7XulheDg4Po6enB7t27sXfvXvz7\n77/o7e0ts6uncU6DKVME+XweV69eHf88MDCA+fPnT9XtU2F0dBQfffQRnnnmGTz++OMAxiLU9evX\nx/+dO3dujXs5hjNnzqCnpwe9vb0YGhrCrVu3sG/fPty8eROFQgFNTU3o7++vq7HO5/NoaWnBkiVL\nAAArV67EwYMH63aMAeD06dNobW3FnDlzAAArVqzA2bNnMTg4WLfjnAZTpgiWLl2KP/74A1euXMHI\nyAi+//57LF++fKpunwp79uzBokWLsG7duvFjjz32GI4dOwYAOHbsWN30/aWXXsKePXuwa9cuvPnm\nm3jkkUewZcsWdHR04OTJkwCA48eP101/gTFSbWlpwe+//w5gzMkWLVpUt2MMAAsWLMC5c+cwNDQE\n59x4n+t5nNNgSncWnjp1Cp999hmcc1izZk1dLh+eOXMG7733HhYvXowkSZAkCV588UUsXboUO3bs\nwNWrV7FgwQK8/fbbmD17dq27W4JffvkFhw4dGl8+7O7uxuDgINra2rB582Y0N9fP79D8+uuv2Lt3\nL0ZGRvDAAw+gq6sLhUKhrsf4wIEDOHHiBHK5HNra2vDKK69gYGCgrsc5FtkW4wwZMmQ7CzNkyJAR\nQYYMGZARQYYMGZARQYYMGZARQYYMGZARQYYMGZARQYYMGZARQYYMGQD8P/r8nC267mfnAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11da25ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's store the number of values in our Gaussian curve.\n",
    "ksize = z.get_shape().as_list()[0]\n",
    "\n",
    "# Let's multiply the two to get a 2d gaussian\n",
    "z_2d = tf.matmul(tf.reshape(z, [ksize, 1]), tf.reshape(z, [1, ksize]))\n",
    "\n",
    "# Execute the graph\n",
    "plt.imshow(z_2d.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ksize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"convolving-an-image-with-a-gaussian\"></a>\n",
    "## Convolving an Image with a Gaussian\n",
    "\n",
    "A very common operation that we'll come across with Deep Learning is convolution.  We're going to explore what this means using our new gaussian kernel that we've just created.  For now, just think of it as a way of filtering information.  We're going to effectively filter our image using this Gaussian function, as if the gaussian function is the lens through which we'll see our image data.  What it will do is at every location we tell it to filter, it will average the image values around it based on what the kernel's values are.  The Gaussian's kernel is basically saying, take a lot the center, a then decesasingly less as you go farther away from the center.  The effect of convolving the image with this type of kernel is that the entire image will be blurred.  If you would like an interactive exploratin of convolution, this website is great:\n",
    "\n",
    "http://setosa.io/ev/image-kernels/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512)\n"
     ]
    },
    {
     "data": {
      "image/png": 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8HP/xH/8BYBSKfve730VnZyfq6urwxS9+EUVFRQCAH//4xzhw4AAikQg+97nP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wGYZzDZbBY///nPhaGUy2W8/vWvx+HDh9HQ0IDVq1djcHAQo6OjGB4eFm/Em9/8ZqRSKfT19SGR\nSGDr1q0YHR3Fxo0bce3aNVy+fBknTpyAx+NBIBDA1772Nfh8PrzhDW/Al7/8ZRw+fFjqZtxzzz3o\n7++H0+kUs8Pn8+HcuXM4c+aM0EkqlUJzczNqamoE36BZOzIygsHBQQEN6+vr0dDQgHg8LvVD161b\nJ16MK1euYHJyUgK8OD7UVILBIFavXo1AICBh8TSBCKwyNkID89R0aWbk83lMTExIABwLGzc1NWFi\nYkLuQU2D96HWEQgEJPGS5sz4+LgAx6ablH2geaZT+qlp3UpbVgyD7i/tSmVhWh16Ozc3Jy4mFjnh\npLFwDoFTt9uNQCAAp3OhGAyweFtBLjw+Wy9qO1WfzWQGdiHbBMSWwkfszB27e5vNjpHoPhYKBezd\nuxfNzc1obGy0MEa+q059djqd6OrqwrVr1+R7qVTCgQMH8OSTT6KtrQ1vfvOb8cILL6Cnp0fyTCKR\nCLZu3YqjR49idHQUbW1tKBaLAo7mcjmcOXMGU1NTIvF8Ph++9a1v4T3veQ/+8i//Es8//zwuXLiA\ngYEBpFIpDA8PS72HYrEoZQ82btyIUqmERCKBSmU+8Oq2226TneUZtTo+Po6jR48iGAyiqakJzc3N\nmJ2dRSKRkP1VZ2ZmMDU1JTvSV1dXSzAfAeJwOCzRky0tLYjH46ivr8fU1JREJsfjcaxcuVJiSsLh\nsGi8VVVVounU1taiVCphenoaLpcLiURCNolubm6Gx+MRj5DH45HCPdygmtXvQqGQaBTcbFpH7mog\nkyaZBneJ4wD4/U5vp21FYmP5PbfbjZmZGdkXM5PJCCjKrNN4PI7m5mY4HA5hOixBD8zveTI0NLRo\ncS4l3TUzYTMjULUJwuvsNA7zOfzdDAayA0fNzzcyUfRvjLVgbIMuIKu1B22eeb1e9Pb24vLly/I8\nt9uNjo4ODA8PY/Xq1VJZm1Lv+vXr2Lt3L9xuN15++WUUi0UBrtvb26VWBPvE54XDYdTW1uI///M/\n8fDDD+Phhx/GM888g+rqakSjUTGRisUihoaGRKrGYjEJmCJguWnTJpw8eRIDAwNiljCeY/369fI9\nnU4jlUrJtgncua1SmQdtg8EgYrGYZKiGQiHE43GR+jyHIezUatxuN+rq6hAIBJBOpyXmgZW0qCUQ\nXI7FYgAWqurTrAbmNQiaVfyjN6lcLguD4/NZKS4ej4t2o3EMClRiSpFIBA0NDQAgIfC30pYVw3A6\nnZiamoLP55MdqsghCYBVKgtl+fREOBwOAT+prjEsOJVK4dixY2KncjHYqWM3YiDmAtX+cDsmxM8m\n6GmHj+hz9TlsWoMxTR4yAFNLCQaDOHHiBDZu3IhDhw6Juu33+xGJRCRAiGYCAJw8eVLAQBLi+Pi4\nIO2XL1/GxMSEBdTbtWsX+vv7MTAwIBsfd3R0YPPmzXIfaiw0f3SOyNNPPw2Xy4V77rkHv/jFLyQF\nn3Y/azgwFFoX2aFtvmrVKgwODgpthMNh9Pf3Y/PmzYhGoxJPocPDdai3LpqjAXOtsRLAzOfz8Pl8\naGpqQrFYRCAQQCwWs1Qg0/lL5fJCIR+aGzQL6EWioKtUKuLapjZGLTAUCsl7BAIBRCIRKS5FRqEz\nYTX4yn61t7fj3nvvXVJrfa22rBgGbUefz4dkMim+YjKA9evXC3pPhDeVSsnf+Pg44vG4qIbcH2Vy\nchJdXV2WEmrk3lpDMPEC87jJNLRZo00dU4swATE20xTSZpBp2pjRofp6u/6RAFnK7vLly0JUDOJi\nPgP7mM/nMTQ0ZAkGu/POO3Ho0CFZ7JlMBm1tbaivr8ePfvQj3H///WhpacGxY8fQ0tKC69evi2bI\nDZDL5bKAomQUNB8p1b///e/jz//8z/HmN79ZiuXwvf1+P+LxuMRjMF+Ei8Hj8eDuu+/GyZMnLdsO\nFItFDA4OYuXKlbJoOXbalte4A4WQDlpj9KqeJ44H3cKU8MCNs051pLJOLrMTILr8ALU6zXg0PWi3\nrzbp2WcAllR40yy+2basGMb09LRwS0YdcrsC2oasmlVdXY1isYhkMilcWBebJZinXWm6mZOkjwML\nyWY6cIfNXOSmuWFqCa81OVpDML0rZjOBWGBxAhy1sKamJtx+++2oq6vDbbfdJntu6IAdAsQAZI8S\nPQZ33nknnn32WQEOK5X5zZgvXryIQqGA++67T9D2WCyGa9euyfV6z89YLIZEIiHvod15rMT11a9+\nFR/84Aexbds2vPDCCzIeurydjhvgonU654sQ33HHHXj++eeF+bndbnR3d2Pfvn0W5qVzbrgQTYag\n55OaDM8FFnCCcrksNKcFBBkuMRUyCC5u0pTJVHgen0H608xO05Q2QzU9ambDtUHGSDOJfbmVdmuZ\nJ//LjYg3B8ntdsvGvHNzc5iZmcHMzAySyaSU32OwDTBfeOXatWuS88Ad2Fmmz+T0GtwErKbBUsAn\nf+eEm7kGvLe+lpOitQ7NTHRgzlJMxjxuajv6dxJsdXW1ZU8Y7V3S9jED32ZnZ2UPVd5r3bp16Onp\ngc/nk4K9+Xwehw8fxvbt27F27Vr09PRYpDD7o6W3w+GwANPEKLxeLwKBADo6OrBt2zZ8/vOfx9zc\nHN7ylrdIPA09FST2eDwutUA5tgR4aa9PT0/D6/UinU7j3LlzljQBjh0TtrS2QQ2IvzO4ien1TPBi\n4hbfTdfBoEnCY3qeKcg45xpL0lol703siX3WppNmmCYNkZFR02P+jNZ8SAe30paVhuHz+aTkG/Me\nHA6HBGhpNUuHwHLwSDycTHpI6N/XeQPAYs8Gm5bUphQwVUFzwZreEEp7O5zDZEQm4Gn+t9MuTFOE\nfSTBE3Vn3ggXrq7NwD4Seed9SGAzMzOIRCKIRCIoFAo4f/48Jicn8Y53vAPZbBbj4+PieQCAiYkJ\nKYqbTCYtqeO0wfWCoURcsWIFisUijhw5gpqaGtx111148cUXxVRlI3ioGTTjF26//XYcPnxYzvN4\nPDh8+DD27t0r8SVcZJwrHTKvMzjN+eU76LnQtKfHH1iokkVPhwlm6rnS7wIshBjoOAnGVuitRPnu\n7Iveg1VXQuf9dY2S/4lZsqwYBomeDMJuQfOzHjAtxTVB+v1+9PX14cqVKzd8rl10Jvtj9k8ThJ1E\ntVvQ+t3s7mvHJEyzxGQ45jW631SjuTs5z9Ob3XDMuEB8Ph+OHz8ukpyAXH9/P4rFIiKRiCzonp4e\ntLW14bbbbsOZM2dQX18Pl8uFtWvX4uzZszh//jyam5tRKpXQ39+PsbEx1NbWIhAIWNRtNs6Zy+VC\na2sr4vE4vvGNb+Cxxx6TqllE86lB0I2otQKPx4M9e/bg5ZdflgJJTU1NGB0dRXd3N1pbW4U5clwC\ngYDUBDGLIusqVloq8zMXsGaG+hzNkDSD4BhrWtZzR5DT4Vgo/svnMWqT9zSFHeeW+47oe/OdOQaa\njm62LSuGQaKg35hl0JhDwoFmkIzGDKi+0cdfKBQwNzcnNR01WqwXtZ1qf6O2lAmjj9lhG4B1Udvd\nV6uHJlhq93w2zUR4n0gkgpaWFkQiEanAZEYBOp1OC8M4e/asRcJt2rQJ586dQ6VSQV1dndT57O/v\nx6OPPgqPx4Px8XEB0cLhsFTFWrVqFRoaGtDa2orW1lbLFoaUunpx8pnlcln2Jfnud7+Ld7/73ejs\n7MQrr7yyJKhH5pHP5xEMBrF27VqcOnVKMASPx4NnnnkGH/jAB2ShUEOlG9ZuAfE52vOghRhzVorF\nonigTIyAoDLpl3gC78G+8Nl0AZsgpt4Fj9qVTocnk6LmxuQ0zrX2ouhM1d/rSE9do0GDTQzI4YLi\nAGr1DliI+CSXTqfTsgWBViWBBcBLN9M8oDTgvc3zzPPZTJPH7phpkvCz/q6fv5TGoZv+Xltbi/b2\ndgALqrZ+f13NmgQ2MDBg6eeuXbvw0ksvoVKpoKOjA+l0GidOnBDV/9KlS1IlCoAlg1L3mQyCc8g5\n0glYXIBcbI2NjUilUvjOd76Dxx9/HFu3bsUrr7wiMRKkEZp7jGJ0uVxYt24durq6UCwWhaENDg6i\nu7sbDQ0NloVCxkEcg33gmHBcuX2BBg9pNjABkotQaxy8vwYZNSPhgiYD5TuQsdB0pIDk/cyFrgVn\npVKRuQAWNCQKXmJ7dJXfSltWDIORg6FQyFJ92ul0WtRrTqieCK16U8tIpVIYHBy0NQE0JmKCYSZz\n0b+ZuAN/0//ZD/6300S0dmN3TztVc6n+63tXKvPRj9u3bxfJNzc3h6mpKcECTM9AMBiUSEney+l0\norm5Gf/xH/8Bh8OBtrY2XLhwAd3d3di/f79km1KyOxwO2VOGY8oFrTVELkrNxHX/tSejsbERvb29\n+MY3voG/+qu/woYNG/C73/1OgFuOg/aclMtl2YLx2rVrsh2kx+PBSy+9hIceeki0Aa3iU/3XJga3\nNGSf+a7MKeH8MU5Ij6uW4PysMY+5uTnLYtXbDWhvDOeX78uoV61pMW5DY2VkGIVCQYQFw/iZ5fp7\nj2HogqQkBLpKtTTRqhdgzQHhwAKQLRLttAOtYpqah7lY9bWaAdi1G5kM5rV2WIVd38zz7d5H9/2B\nBx7Ahg0bLIlb/I25FVriOZ1O2UyYjd4VljKMRCI4cuSIBFhNTEzIQtCahGl+UQpz/lwul2ADZA4k\naIZocwHOzc2hrq4O/f39+NKXvoS//du/xeTkJI4dOyZ919WlKEn9fj/a29ulsG8ymURjYyMGBwcx\nODgokZpkTLyOTIyueD2+ZDJutxuZTMbi2cjn80in04jH48KgGJylzWzdR5pB1BaomZD5MSubXhFi\nIcQnGJOhzQ+dP+X1ei2mCIUoc1F0seVbacuKYWhiczqdQlg8Rjcg3UlaA+GEUxI4nU5cuXJFJs98\njp10tusHv9stYtOeNu+pj2mMYqn7mwtO/2an7QCLa016vV5s2bJFTAAuDAYTUfLzflxwZ8+etUj5\nLVu2YHR0FNlsVvaGOX36NLZv346VK1eip6dH5oLRo7qGBZ+hy8IBC/iUHktKdT0OJHy3241169bh\nzJkz+OxnP4tPfvKTKJVKOHfunEhiE3D0eDxobW3F+fPnpWAuzYb+/n7cc889og1RIJHeKpWKZZtE\nMgQAlvGjp0VrZdQytAbDMaDHQhfvpVeDi514Cl3P9GKR0VJTAyAmEmmf7lwNLLvdbsv2k5rhMEbm\n9zq9ncTt8/lExXI4ForiaMlKggIWQCMdqJLL5XDu3DlbkBJYjBnwd20q6HN10xJTMwKTWZjmht3i\n19fpAC59rdlHO985r2M+je4DbWINpJmg2vDwsAXk27lzp2xRuGXLFpw4cQIAsHv3blQqFVFr9aKl\nqk4JSAmmn62jIDWh6/kko9Wq/caNG9HV1YXPf/7zeO973wun04kLFy6IJNdYVrFYlISziYkJVCoV\nyQq9fPky2tvbJdeDC5Z0pAF0MjGm+pOpENOgizOfz0vRYppgNIOY66SxDy1IqF0x5kPPMftFhkZm\nxvHStFGpVKQeBtcSTRUyolwuJxsZadfxrbRlxTCo3nISyW21BOE5JqpNjk1CGxgYwMTExKKgKT3A\nGtBcyjQwr9PHTPXb/K/bjSZGE8hSeIjJyEyGxv687nWvE6mtx5U1RQh0kbgdDocwBv0+GzdulCK7\nHR0deOaZZ7B+/Xrcfvvtcr5+PqW1dhlq4I/1O4HF9Rw0AAksaAlcsFxsnZ2dOHPmDL7xjW/g3e9+\nN4rFouwuz0XHZ3LjpEuXLllqWkxMTKCvrw+bNm2SrE2tlbIfBIrZSFcM5CIQyc9MeNR4SCQSsZgL\n/M9+8v05t8RUqAVQA9Ch6hqM1dqHBo45jxS0FKJkenrXtqXM76XasmMYGrHW7iKN6pK7Uk0jAyGR\ncpcnXg9g0ULW5sFSNjj/a4lgpx2YDMW8L7/baTj6d/3frulzdD/ZB0Yhjo2NWcC3YrEoXgQGZnF8\nC4WCgJds1AySyaSo/06nE7t27UIoFEJXV5cldoJEx5wfHTmpJT89W1TjCchp5lhV/UMAACAASURB\nVKavp61Ps8HhcGDbtm04deoUfvKTn+CP/uiPZOcyqv4EV8vlsmyNePXqVZRKJSl3xzodlPja3ucC\nYwQqa5GSYZCx8o8FhbndgnaZahCVzMfUhrTgYh+0N0ZrhBpE1Z4mhvwHg0GLKUQmQlOKuAjHXseX\n3GxbVgxDq+B8MRIOpQ9VRCLFJm5ASdLY2Ijm5mYJdebv5sK2MxfsflvKLGGzwxnsfruZCTJNF5Np\nAdZK4/zf0NBgGTfGV9AkIePV5ojb7cb169ctLuvGxkbxrDidTly8eBHt7e247bbbMD4+jomJCct7\n6b7wTy8cErbGnMwgIo1F6esoCbWJun79enR1dSEQCODBBx8EAAwPD0sEKxc4i97QA5TP5xGNRnHt\n2jWMjY2hublZ1HUTcyGTpTTXdKFNGS5+MjXtwqQJwWdQopvuWTJfXsf+a21CPysQCIgGzvtxb1c+\nn9oKkwy1xs7732o9T2CZMQwSknYr6QlhNJ+2LQEr0fH6eDyOu+66C7/+9a+lGjXP1ZLa1BxutLjt\nzrVjFKaaZ5ou2jY3m9097Uwfftd93rJli2Rp8ji1AIJ0gUBAFhaPc6sAjunGjRsxOzsrG/X6fD7Z\n3PjixYuLEq+0FsfFRiLVY0GVnv2ljW+qziRmrTnxfjQjVq5ciSNHjsDtduOBBx7AwYMHMTU1ZdFI\nnU4n1q9fj1OnTkkNCeJk165dQ11dnXgrAoGAzIkGshnybsZTMLERmI8WZSgAMC+90+m0hAJQG+Ym\n4Rx7TfMABI/R9EZTh89luUEuet7bDAVwu90yVgyrpzbIUgYEWm+lLSuGYVa11lJILzIz/sLj8SAc\nDsPpdMouXKVSCStXrsQDDzyAZ599Vmo8AvaBVfq4xhT4XPO7/jMZyFImCrDYq6Gfp9+VkkcfW4pZ\n8XoGJdG21sFtZKi5XE7qKlCVZZ0R3m/t2rWyrwgrXd15552oVCro7e2F3+8X05EmgwYyAWu4M6Wt\nBq/pnuQepxQKZCgEE7XAoAnF/ldXV+PHP/4xAoEAXv/61+Of//mfkc1mxYXIOh8rVqyQHdQKhYKU\n2Vu3bp24QoGF7STJdNhXgvCa3ihsdPwEmZLW7DjOHCtdElEzDU077CfHjeNDpkNGwL13SCe8N69n\nHRAAslUkPSl8/u/13qokKGAhs5JSSu9I7XK5LFGhmrvSVuSEbt26FaFQCM888wxGRkYWLWZKNW0G\nmViBlpR2Ut5kPKa5wHtRE9LXaWbEthRWYvddq9LUHHRUrAYU3W43otEogHkijUajEj2pTYvq6mop\nR19TU4O2tjZs2rQJAwMDUoeEROz1eiUQS88fGQqlNe+tIxjZb44vpbb2MlBIcF75TDLFzs5O/OQn\nP0EkEsHjjz+OH/3oR+LBIfPs7OxET0+PBC5xv5OZmRlUV1dLvwCIV0EvKtbhIDMxE8D03LBfOp+J\nzIJzTRyJ+6cwU5jMkhXKyQAY5EVgv1QqSdU54i/M6NVhBNx82e/3i3u1XC6LJsbK+7fSbpphlMtl\nfOITn0AikcDHPvYxjI6O4otf/CLS6TQ6OjrwxBNPyGB+5StfQW9vLyKRCD74wQ+ipqbmpp7BweKA\naXUUWNhvAYBwe3JyzTj4mRK0ra0NDz74IH77298KAKYBJY2P8N78bDIR/m5+fq3fzfvaja9mIiZD\nMfun71ssFrFu3TpEIhFZZHocaSvzNwAiha9cuSLIusPhQCQSQTgcxuDgoLhKb7/9dgSDQfT396O5\nuVnmGYAlRZ2eGL2nhzYrtGZChqA9YA6HQ0rKVVVVLQKy2Weq21wc1dXV+N73vge3240/+IM/wG9+\n8xsBON1uN2pqatDS0oLLly9bXO8nT57E2rVrxc53OBwIBoPIZrMyJj6fT57pdrsFWKWpwHfl+GrN\ngteQKZB5UsPg3PC7dttq5kSAkjgEYN36gPfS5gXHJxAIWKqNk1FqfORW2k37VJ555hnLRrv/9m//\nhje96U344he/iFAohIMHDwIADh48iHA4jC996Ut46KGH8N3vfvemO0N7jNyZBMdwVtqDAITDc3Jp\nV7KeJFOzSVzNzc14+OGH0dnZKanGdsCkiQ+waWTbzqTRjECr5vrd9H/zHFPz0YzDNIH02JChbN26\nFbFYTIoH0SYnQYbDYbGRKeFLpZKknvNZ9fX1cDgcuHr1KpzO+epcd911F4aHhy2LgIWM9CL2+/0i\nkRkYRVWe2z2QuWi3IQCpe0EtSe/tyrmlNKTGwWvq6+uxY8cOfOc738Hp06exa9cueV9gnqkdOHBA\nwF56Oq5du4aJiQmpq0FhxPoRLC5EDYixDKwfwiAsYgvMetVBWtxhntKdptbs7KwULKK5BMCChdBk\n0QWJOQbBYFAwEadzfquBdDotdT6npqaQzWYxMzMjEb+kB76bz+eTYto3226KYXALt/vvv1+OnT17\nFrt27QIA7Nu3T/aaOH78OPbt2wdgPsjnzJkzN90ZjdqGQiFB971erxAAXX4EmjT4w23gNPqsA3FC\noRDuuece3HvvvVi7dq1FuzDxAR5j08c049DIvm52jIELXWsvJjZiXmMyC15HIqaKnkgkRIJr4DAY\nDEqJu1gshnA4jEgkInur6sJCxEEcDgd6e3sRDAbR2dmJ9vZ2XL58GfF4XFx0WjPg+LPeJj/zN0pr\nSkja+rpYDU0RHYPDuaZLlZgBc47YBxaBvuOOO/D000/j/Pnz2Llzp6jrDORqaWmxAJsulwsHDx6U\n85h3w3cIBALI5XJIpVLyDhRUxC54H4Zva1eqdrOSvmlO+/1+VFVVCV0zBFzHebhc87U7WTmfod0s\nhsNKX6yEXlNTg3g8LpXNE4kEqqqq5Hz+8VhdXZ2lCtpNrdGbOelf//Vf8a53vUtCtbkbNDlydXU1\nkskkgPn8DdbSJDdjJeXXajqoiOpWpVKRAdQqGaUZGYZGhAmSURJptS8cDmPPnj3YvHkzzp07h1On\nTkkdSzY77MHUOHgem7nI7X7Xx/QzbsSs7DQfE+xsbGxENBq17GlBPIcmABkpQTu/348zZ85YSvIB\nQENDg2zG3NDQgD179mBwcBAzMzOIxWIWbxSxAAKcBExpk7/00ktSQlHPi05846bMtM/J7Mj82LRn\nR7sxea9yuYxoNIp169bh5z//OUKhELZt24bjx48L3ezcuRP9/f2iKYRCIVy9ehUTExNirunYBe7+\nTobIxVwul+UdaT5wMeqQcmpkmokAsARnsUq4w+GQ0HS6ZzVTZlawBiq1eRWNRmXvEq4RhoQTV6EG\nH41GZa3dantNhnHy5EnEYjG0t7ejq6trEYGzLWWb2y00AOjq6pL7AfO7YO3bt2+RFCZqDCwUJtGR\nhJwcToIG2/TiIiFQEgDA61//epRKJVy8eBHDw8NL9tWu7d27d8l3Zr+1mWGOB4lTMwT+xjGwu8a8\nX6k0n9W5evVqUU9zuZwl3qKmpgZbt25dZIZxn1T2w+FwiGnz4Q9/GB0dHbj33nvR09ODnTt3Sp9I\nkBpwBeYX9V133WVx2cbjcbT/3zR7DS7qOAQyAM5fW1ubAKD6fNKBzlbVgDEXF3djq6urwx/+4R9K\n2v6uXbuwfft2zMzMoFKpIBgMolgsoq6uDmvWrLHQlI4a1TSnzRM+r62tTRYu+2F6w0h/pGfiQwR/\nOZ46ZojX8Vya3lrjZDCeGVau3dKcj0KhgKamJuzcuVNogM97+umn5dimTZuwadMmk6QB3ATD4L6P\np06dkvqP3/72tyXRxul0YmJiQio5JxIJTExMSPHeXC5nq13YderIkSMW9bhYnN8IRm+8oolIR8/R\nZtZ2OjUcBu2YUpfl4f/93/9d6j6Yk2V6O9gcDgc+85nPAFi8oEnMPGaaM1o70N4TLkBKJT3hZIya\n+XGMHnzwQWzZskU0LcYAhMNhzM7O4sCBA/jVr36FbDYrmpbP58Nzzz0nW0fSTffYY49hbGwM3/rW\nt/DXf/3XaGpqwtGjR1GpVGR3cI4tg4wSiQRKpZJsNpVOpy0AK9+P6jsRfY4xk7O4Uc/+/ftx6NAh\nMbeIaeh6mMQQWGdTR5Z6PB709fXh+9//Pj7wgQ8gnU7j1VdfhdPpxCuvvIJf/vKXsgjpbXvssccE\nP6CrOJFIIJ1Oi/TnXOoNm4vFIu677z78+te/tkh/MkPuQq/NFzI/c+510BYXOBPZ6LniM7V71+Fw\nIBaLCa0wyUwnHJIB7927F7/5zW9kXJ1OJ+6//348+uijr8UKANwEw3jHO96Bd7zjHQCA7u5u/Oxn\nP8OTTz6Jz3/+8zh27Bj27NmDQ4cOYceOHQCAHTt24NChQ1izZg2OHj2KzZs331RHgAVfMYNLKpWF\nXcG12lupVCwoOQvTciLpN6eE0G5Y7dOm/axtYq06a/84ub/WBrRk5sLQfaSU4m9LmTWmhmHeU5/L\nRiJjZa2mpiZLzAL7wkpZNTU1smiZ6Uh3KomZZfiOHDmCaDSKbdu24dy5c6iqqsLMzIwU5U0kEpbi\nMnS1kmHRw8EYCxKtmWBGpkmzJRwOC9DH/8CCN4FxCKlUyuKdACAFf+mmbWlpQTqdxhe+8AV8/OMf\nx9q1a3HmzBnBDaampjA7Oyt7ifT392PTpk0SC1Iszu/T63K5hNb01gw6cpaALgFVvi8BfGIzpE0y\nELp4SffFYhHT09NCh7xO4yY0kcrlsuBTpHG6X/ksjjkDyLQ2Rpzp/1l6+zvf+U584QtfwA9+8AO0\nt7dj//79AID9+/fjy1/+Mp588klEIhG8//3vv+l7MpSVxGdG/HFxAwtRa7R3AYik0q4pHYpMLEOn\nDQPWmAiCUUNDQ8J0tFcCWFjQOsTaNBe0ZsDvZB5a9dUmE/vCZmo8prZTLpexYsUK1NbWiplAcI7j\nRgCP7spkMgmn04lz585hcHBQCDYYDGLDhg1oaGjAhQsXsGPHDni9XkxMTMDr9cru5vl8HoODgxYp\nyL07qXVmMhkJVabGRCZGCcg+Mv+E867nYm5uTphasTi/6dL09DQ8Ho8UsqHXwu/3WwrzAEBHRwcq\nlQqeeuopfPzjH4fb7capU6ewfv16nDhxAlVVVdi7dy8ee+wxJJNJnD17VrRiSnpiatTMyAgoELg4\nTWbIvjC2ghKenhQKLu54RlrnOxHYBxZiQ3T+CDdb4v3IMLR7nvRXKpUQi8UQDAblfozl0NjdzbRb\nYhgbN27Exo0bAQB1dXV46qmnFp3j8XjwoQ996JY6wUZXExFqqlZ0sVEyksPryaXpQSZAG14vUu2i\nY3QjsFDpi/ZlMBhEQ0MDBgYGREsx78OmObZ5TOMQ/K61Cc0s9DGzaQZhfucGwzqIx+FwSF0EEhEZ\nFk0dZpvG43Fs374du3btwv33349AIIAdO3bgrrvukuhYjjuwsNcIzVPGHVAi6pgPAqXM3GQgEaUk\nJWwkEkGpVJJd77iXLr0klI50D1K6UvpzAdB9TC9MuVzG2rVrce7cOfz93/89PvKRj2DXrl3S1zVr\n1uDxxx+XcoMXLlwQt3AulxPTiaAhQVDNIAh0hsNhwY5IixzraDQqTC8UColblrSkXamkdTJFLfjI\nfMmsqIFoE1sHzmkQVeMec3Nzlt3vbqUtq0hPDSoBENcbmQBtN22icGAoDUi0PIdqILkrQ4c5ASw9\nDyxoBfl8HolEAqlUSrwIGlsxF7o2IczG30x1kM3UInjMLu5D/66xDsYokNn6fD7xaPE+qVQKVVVV\n4tmYnJwUz0JnZyfuv/9+bNu2DeVyGY8//rjs0E6bm4taa3a6TmgkEpHdyTmeZBaMlWGRXp2opZmX\nx+OReBsd2EWa0Ko/I0wpiSkg+DsXaKlUwtq1a1GpVPDDH/4QH/3oR7Fu3Tq43W6cPXsWPT09yGaz\nOH78OI4fP47du3cjEonIYmMGKGmsVJrfjkG7OnXGK/EVenloJmjhRA1Cx0bQtKM2ofGeubk58aZM\nT0/LOOq5oAcFWNhgmYyeuB29MfRs6Tygm23LimGQ2+pIQh7n4HIQqCVQTSMARFWN99AgG79TY8hm\ns0gmkxJFyMVF27ampkYm0VzQJEjAioDzj9/J2LT5YYd0awzD9BLp/5rZxONxwS5oi5oAr1adGbBU\nLM4Xx83lckin03j++ecxNDSET3ziEwgEAkilUuju7kZtba2FKfJdAYgKTFOC4dhUtymRp6enLeYL\nFz+xjnw+j2QyKSq/LkgTiUSEAWlMQOeLcPHR+5XJZGTOGMOTz+exZs0ajI2N4cqVK9i8eTNqa2tx\n8eJF9PT0SLJYNptFbW0tbr/9djEfMpmMBEs5HA5MTU1ZtANqXKRDajecJ/ZLFy92OBySEEk61qEB\n7Dfpg3uuMjCR7litsTIrlpo554gCQmuCtbW1yOVyQie30pYVw+D2duTMwIK9b2Ia5P4aSwAW/Poa\nKaYqzN250um0gHejo6Nyf0p2EmgkEhHOrjUBU0PQ7jf+6YIlwGKmok0Sk5GYWgvvYZoxiUQCq1ev\nhtPpFA+GHjcCc9wtjNpYsVjExMQESqUSJiYmkEwmcfXqVbzvfe/Dpz/9aYyPj+ORRx5BJBKREHHt\nneIC4uIIhUISVUjQsFKpYHR0VCJP2Sdu/kzPA/fPdbvdIn3z+Tyy2axEIWr8KJvNiubIRUY8gHY5\nr+HWCtRMa2trBbsZHBzE9PS0uFyZ19HT04O1a9fK/FKrYX8JyBN7YZEcgu4ejwfJZFLAWQBiqjDq\nmNs2kj7JOKmJ0Nvk9XpRKBTg8/nEQxIMBoUxcl9avnMikUAsFpPx4ziToZJxNDY2olgsIpVKieZ9\ns21ZMQyqoFqSVFVVweVyYWpqSlQ+FithowZAaZ1IJDAzM2NBn6nCkdgZ/TgwMABgIbWYUiKfzyMS\niaC+vl4qNt1I6msJ7HA4JESYUkRjKJT62kQxzRp9T9Ns4W/BYBDxeFykNseE4fHsC7CQOxIIBNDd\n3S2eCZ5DyXT06FF4vV4cPnwYq1evRn19vdjcGoyk5ymbzQqz0FpauTxfYoCaCKWijnOguk53KiUk\nzR2aVZTkjJ8ghkGgN51OY2ZmBvl8HvF43OIJGhgYkB3bEokEisUiRkdH4fV60d7ejpGREcECotEo\nrly5guHhYcl/cjgcGB0dhdvtFsFD4VUqlWSHt76+PtG2vF6vBCzSdJmZmbHgYHV1dRJVyv663W4k\nk0mEw2Exg4hnMOyf5g7D4nVZS/aJYw1AQFGaOnNzcxJkybG/lbasGAYAMRWo1nOCqE3QNqRZwoHQ\nngsi1/SBa0ZAe5+q5OTkpDAcnexDgIuxGmZE5P/X3rnHRnqdZfyZGXt8nxnP3R6PPWu73iS7Sdps\nViQpkKRBRARQIxQiAUJC9B9QSGkBCaiE8k8LiJamKUGRqChFgJAQKJEiEEWNkvRCEiVkN7vZ+3rX\nXt/Gc7/4bs98/OH+Xn/jbKhXdBNH8pGiTRyvfeb7znnP+z7P8z6H4Z7n9QKJGzDlJN2t0+BF7wY9\n3f/uxjNA3ePxuNWkZE/uE5uNxgm8srKigYEBNZtN00owl6GhIVWrVS0tLen48eO67bbbLI1lrm51\nJyI60nZocOYKzcnnw/QI1ePy8rIxG41GQ9Fo1NJ0rmUEk+GUh9ZkrK6umv6AZ+D1epXP59XT06P2\n9nYNDAzYRiwUCnZC12o1TUxM6NSpU4ab8Pto4ac/gxO+u7vbAm0oFLI7fvHKBBODtaPkDQQChrMQ\nJNxCMAIdn4cA52aCCJ5u7w36UDgg8OrkvS0vL2toaEjSNp5E+QWr5ff7rVTa69hXAYMMgegoyYIH\ntXS5XG6JoCwgMgRSRXoQiL7UeNK2hoHLeqvVqoGqbnXf6uqqIpGIvF6vBgYGWk5Zvt89790lhXvw\ndVJpgpM71d6dUez+Oe5SRtrOkBKJhObn51Wv140xYFEThHp7e3XbbbfZhnjnnXc0NTXVgpM4jqMH\nH3xQr732mjwej2KxmCKRiLEhAJfN5o7bU6FQMNAsEAhYVkGavbi4aNkVaH+hUFAgENDAwICKxaJ6\ne3uNXaBZjqASi8WMaYHFcjca0nwGEEun7urqqmKxmKRtOXW1WjWdCJva5/MZCDw4OKiLFy+2AORn\nz55VOp02KhQqvrOzU6lUyp5FKpVSNBpVKBRSOp02nUiz2TRNCeUFPTtkH11dXSqXy/b8i8WiYUyV\nSsWes1tS3mw2DdPh4BkZGdHKyorK5bKVaUjdeSd8doL9lStXLCOpVqs3tEf3XcBgkUo7oA1fcxzH\nMgpANXdW4dZwkJ4DWoEG89BB/qWdi2/dGxSKFwApnU5rZmbGIj2p8W6a1X0C7v66u2xy/7ebSXH/\nPffX3BQtDEk8HjfAixSdkoESzP35eD4XL158T7lz7Ngxff3rX1dfX59tVrIIAGefz2cNWvQjgDu5\nMyco19XVVaVSqRYZ+NLSkqamplSr1axpCkXjxsaG+vr6tLq6ahkdrt+UGADUlC68D3e/BBstn89b\nptnf369wOGzPGkD1nnvu0eTkpLFj9NFks1kdOnTIaF5AUWnnbhEOL0pQAFhpx3+ku7vbDjEOLFSW\nZFiAz/SE1Ot1w0vi8bhhMJS4/B6eExIB8BQ3O8NamJ+ft4PywoUL9nuvlzX/X2NfBQw2MuAMp83y\n8rKlm9BMXq/XIjRlRr1et00B4uy+38HNWlB/u70b+JO/v7y8bAuANupsNmsP2o1Su/99d8Yhvb9n\n6O4sQpIBr7sDB3/29vYqk8moUCgYrVqv1w0g6+zsVKFQkCR7LtPT03Ziz8zMtMzF7/fr0KFDevfd\nd9XT06Oenh5NTU1pc3NToVDIFrobNJW2s79KpaLOzk5Luzc2dm6K9/l8KpVKWllZeQ/Fiiir2dxx\ntO7o6NDS0pIajYbq9bplBmzYUChk2SPZxNLSkql4OcX5PawhdzaK7Nvn8ykSiZjv5/nz51vA1NOn\nT2tgYMBYH94HuhGuZSBAAlby2YPBoB1ujUbDKGfYD94N8wuFQqbKjEQiNk/3fS/uTmGCIocqAYMs\nEEyHEoi10d7erpGREbvk6yN9kREPaHV11RYnC5D6jwVDXeneRJJs87hTckRC1ODgBWtra6rX6wa0\nuk8o0jhSy42NDfX09BiQ52YwdmcV7iCy++vuf3d/7+7v2V32uH9OJpPRJz/5SdtwyOel7ZvKQqGQ\nlXL8rHq9bpJ5N0jsONsyaq/Xq3q9rmg0qmq1qkKhoJWVFcMO3CAqQcotdeaZ0vvQ29urYDBoaThs\nRTgctlMZoI+yRdq5ErCtrc1ASk7rsbExLS4u2kZgfn6/37p13YCee5OEQqGW8pPf5ff79fM///O6\ncOGCrYloNGrBGAyBsmRjY0PFYtEavlC0SjuaF9YNbEUwGLQ1srW1fatbIpFQrVYzzYzbmoBSeXBw\nUI1GQ+VyuaXdHaaPvwsuUa1W7dKptbU1BYNBdXR0mJ4mGo1ay7vX61UqlWqxlNjL2FcBQ9pGvP1+\nv2ZmZgzU7O3ttayCiAgw5vP5bGFS67rbhskwSP+I4pubm8ZvM9jELNqtrW2PBLwRUfEB4CF53g16\nStc37r3e/9+NTez+7+sFHk5GKE1pu22fOhU3KDZ7IBDQ0NCQHMcx3xJ3GXT77bfrnXfeUbPZtH6R\nrq4uLS0taXNz0+wKYK/YDADP/L+Ojg7z0+jr6zPF4/LysmUpoVBIlUpFkloEYQScRCLRQkn39/er\nXC7LcRxls9mWZ+im1TkUoDYJcgT0er1uxjEwMvzO4eFhZX7o+QHY2Gw29b3vfU+PPvqoyePJDvCw\noAwBBOVUd9tJurtu2dgcitggunGZcrlsuII7EwHErFQq5kvi8/nU399vOBPZGe+eUiUUCtk7ANPg\nOexWI/+osa8CBi8VykvaaYl2BwOwB1KztrY21et1o+0wSiXNc184I8l4b0BMtw7DfQJRmkD3UfIQ\nLBKJhHkrXo/lYP67QVBpJxBcL7vY/ffdwQfsAjBteXlZgUDATruenh4r6aRtfxIWSltbm65cufKe\nOX7iE5/Q97//fSvVHMcxdkjabgOgpg6HwwYclkolffzjH1ez2VSxWGyxiVtaWtL58+d16623GkXN\nZwA/cHdu8p5yuZxuvfVWS8XZwGSK1PFcgbCysqL+/n6trKyop6dH5XJZY2Nj5tKGA1UoFDJcB0wG\noLxYLOree+/V1NRUS3BBKp5KpeywQfvAYcKG6+3tNQl4R0eHqtWqrQfKCwBSNr7X6zXNBocSilbH\ncYy16urqUjQafU/vDcDo6uqq+vr6bP0EAgFryGQ99Pb2tjxPfhYHzl7HvgoYCE9qtZqlhlBKPNCl\npSXNzs4qnU5L2l6AqO9QHgYCAStJeJhtbW0ql8stwhZwCE4psgi3yhSFKAthbW1NkUjEFh4nWb1e\nt5KJeTHeD9PYPXZnINfLNAAFFxYW7KQjRS6VSurv71ehUNDly5fl9/stOFarVaXTabtTxD2/j3/8\n4/qP//gP+/k8n/HxcUWjUQMrsXuTtinNRCJhjEu1Wm3ZQJVKRb29vZqcnNTKyoq6urrU19cnv9+v\nhYUFxeNxSdLY2Jjm5uZsM6K34Xm5y07K0M7OTkUiEWNIKBempqYUj8c1Pz9vpQI0JxgCz5jyk7J0\nZGREAwMDmp2dbaHp//M//1OPPvqo4WednZ12wVMoFLJ3ThCiyYuygXfEZ0D3AWhL8OG0J/hDx/Ic\nwCnQGAG4BgIBBQIBLS0tWblHMKNXhAuYZ2dn1dHRYfZ8lIE3MvZVwEAcQx2Xz+db/BCIvpubm1pY\nWLDID8AFgoy0m83rttuvVCpKJBLq6enR3Nycce/usRtfIDKzOInwgFwsAGgrTlnp/c2B3+/r1xvM\nx23BXy6Xlcvl7PSdnZ3V4uKiZQbpdFrBYNDoto2NDZ0/f94AOj5fT0+PwuGwZmdn5ff7NTQ0pHQ6\nbTS0x+Oxpi9JLRqOWq2mWq1mCxO6lbnSOQxb093dbTZyZAQzMzOGR5BBsF0FbQAAIABJREFUSbIF\nT1Dn0MBjor+/X729vYanlMtljY6Otoi+qNup4ykpRkZGtLy8bIFMkqLRqO666y7NzMxIkp2+7777\nro4fP26HV61WayllERaChQUCAZXLZctKyWRYf5TJHR0dKpfLLapPvgfGyN3l6/F47D3BosTjcTM7\nzufz5vlJplipVBQMBq20hP4m0/L7/Xb3zF7HvgoYsBJu9RrMhpvilNTCy5PeUmeS2oJCY2grbesX\nisWinQK7AUvKHP6hXAEY5eeTWgMkEigA8NxSX362+9/5DNcLHO4shMwCEZkkJRIJBYNBdXV12YmM\nkY3Pt33rG9gAHp4jIyO6cOGCnYR81nQ6rXK5bMrYVColj8djnhN0AvM8I5GIgYAez05XLGluMpm0\nwLq0tGQnLoEfDAAqnLqbGp9eItgGAgbgIyXhxsaGZRuOs+0qRjZB6eI4jnp7e02YxWm+sLBg7Bul\nw/LysjKZjKLRqEqlkhk/bW5u6uzZs7rrrru0sLBgwcgtS/d6t42aEHrlcjl79wQ9dy8TJWNvb6/9\nfTJgGuj4DEtLS1aWwGx0d3crEomYOI4sjCCDkI6g7c5u3fS2pBbN017GvgoYZBbUgJIMuHFveFD+\ner1uaXKj0VAymbQXRYlRrVYNtUYCTIrHJnczHgw3vuCWArN4PZ7t3gJALFqL0ZBUKhWVSqWWksKN\nR1yPIXEHD3d2xElKauv+d8A7ZOLVatX+aW9vN5C2XC63WCJK26Dj8ePHNTMzY5JiFjSLj+/b2Niw\n0xRDYWTM7hu+YCo6Ozs1PDxswiuATS5HggEDpCUbokxwZ3JuhysAcMoR97UDtVpNxWJRoVCoxcAX\nZSOBnL9Tr9eNranVahoYGNAdd9yhl156ybLdrq4unTt3TrfffrsikYjV/vS0AF5euXLFTnCCKJkd\nFHAikZAkKw3od+ru7jZVLJkbmRNBkS5f3g+ZFopNN7i8ubmpSqWivr4+MzsCWN3Y2LAs6gM10LkZ\ng5OfhhpAG3cLMXWfu62betXj8Ri1BErc3t6uZDJpWUgwGGxhTdjgpI3Qf256lUwDJSgBjIXgZmI4\nSdzAl3vsxjN2lyn8Hiz2UFOymd0KR+pogioyXxYeN4tx+pN+un/nsWPHdO7cOWvOAguhK5IGqo2N\nDctmcNTi2YXDYRM1EVDBG5rNHWcvUnLe39bWlgWAcrn8Hkervr4+88jo6urSwsKCifV4n/hLFItF\nYzfm5uYMi6FcQ6PAKUsg7u3tVWdnpwHahw4dUjgcVqVS0fr6ugYHB1UoFHTp0iUDY9vatu86yefz\nxsigLgXrIuAUCgVrNWDdxONx+7wcBgC/AM4cSFDCiNaYG78LqUAoFFI+n1etVlMgEDDxXVtbm4LB\noB0CXu/21RHSDj53I2NfBQw+SKlUMsAuFAqZbR++j/DU1MZQW4BEgFtEZDa3uyUamsqNN7jVl26B\nDCkzpQmLHwER/833Uu/u1ltcjwnZrdugnCDy8zvJJsbHx9XX1yev12sBAV2Iu/8AfAIgDv2F20fE\n6/UqkUjoxRdflCSNjo4aHbi4uGjIeyAQsM8qya7c6+7uNtwEXIEsh7Sdqwh5TgS6mZkZCzxgM255\nOwwCp+Py8rJRrkihCTDFYtFYIBgCQEUUolgVLC0t2ZqQZOK9SqUix3F0xx136H/+539UqVTUaDRM\n4n7mzBkNDw8bfc0hxrqUZFka7fmRSETJZFLVatUEdLzPnp4eK2mZE7JyaefSLkrCjo4O6xxmraJe\nXVhYsODjvvOFedXrdStB6ONxt8ffyNhXAaOvr0+lUskiOKmWGziiRqM+JBWHXyY4+P1+S4dDoZDd\nwwFgBc6xm97cHXHJMPjZ0k5k5+e5Zeft7e0aHx/X2bNnLYjwd6TWUsddpnBa0mvgLoEIgisrK8pk\nMuavGQqFVCgUtL6+bswQpws3vOEIdfHiRR06dEi/+Iu/KL/fr29+85uWMc3NzclxHN15553q7u5W\nOp3W1NSUpdvItCl9MHRBY5DNZhUMBhUIBAyoJsugoYygsbW1bbWXTqctFQcgxCCGDUlADAQCLXoP\nKEyAxs7OTlUqFcOleI94d8KwMV8oS6TU8/Pz5gjGRicwwBb91m/9liKRiF5//XUrbdva2izrcd9D\nS2DO5/MKh8NmklytVi3DCgaDJkJrb29XJBKR4ziG39FPEolErLyi/4TAXK/XTTrPOyI7h1382Mc+\nZoenO+g7jqP+/n5NTk7e0B7dVwFjaWlJsVjMAoQkS89oaOru7tbhw4dVKBSsPgOjoB7e2Ngw/we+\nhtKT09LdDyG1WvihxSBz4NR2b2DSOXe0Xlpa0tjYmMbGxvTOO+9YMHIrAa9Ht1Jm8Dmhh3f/LhBu\n5l2v1xWPx43SxS8B8KzZbFpLOH0Vly5dMmrv6NGjVoeT0XBi9/T0qLe3VyMjIybBlmSSajp9Hccx\nIdLFixfl9Xo1NDSkwcFBVSoV1Wo1JZNJyzhwmKeLtKurS9PT00b1cSryDgkGsVjMDgBu/aIs6Orq\n0tjYmLlRQdHybL1er7LZrGWtXq/XGJ54PG5MBaDrAw88oGvXrqlWq+nxxx/XZz7zGcXjcU1NTSmb\nzSqXyxljw/sYHR1VNpu1BjuyPNYAdLTHs2OewyYmMMICur1MkeJHo1GjXxFu5fN5y6yhejk4AcAB\naQOBgMLhsD07snTe617HvgoY7s3J6U09Ci7g8/k0NzcnScb908DEpgCYWl9fVyKRsGvjlpeXDfwE\nMOWUd+swyCrc/+wGRkktqZt52ZlMpiXgUCsjcefv8nPdMnNOcSTNbryEFJayg9MIgBEg1u1qRdoq\n7WRKkqxWf+CBB6xFmlOM3w/qT2YAXkLgpCN2eHhYlUrFAifYQ6lUsoUOCAgl6fP5LCg5jqORkREL\neJJMqetuKAN/4WeRZpN9ubUMjuNY3d5obHuFrq6uGtZSrVbV3d2tWCymer1uYjSYhY2NDQ0PD+ue\ne+7Rvffeq3K5bFnq0NCQUc2SlM1mrXVgZGRECwsL1ttCRy9rG4Wwx+NRMpm0A401576ZHRUzuBRz\nHhoasqwM8LlWq2l4eNgCDJoXLp4Cu6OZLhwOy+/3q1QqKZlM3tAe3VcBg6jMJqR2BSHGRAQbNgAl\nFkgqldL09LQKhYKSyaQ6OzuVy+XMPwD1INoE1IkEDMoeAChpp2FJen9hFd8D/gBYC6birpn5Ge5s\niH8oH+ifIDshleZkYXP39PQoFovZfLl1DhGbJCtlUMtGIhE98cQTSqVSisViWlhY0GOPPaZcLqf5\n+XkNDAyou7vbAhaZWygUss/WbDZtQ66vr7fcli5JFy5c0OjoqN0VU61W7ZrGcrlsPpz4QiDFB+gj\nuAJuouegXwRwsF6vt2QmtVrNpOxgEl1dXab74OSFJSPjwBOF9+jxeBQIBPTmm2/qzjvvNFqfd0og\nJa3nc7ibxTY2Nsx7ZH193TIE90HYbDYtE6AFwnG2LRxisZjpRtweoO4si8yF7I0GPRhE1lswGNTm\n5qZOnTqlwcFBK+kHBwdvWLy1rwJGNps1RLinp8cktDwsXiaaDNJ0aET3nRV0LnISSNsBKZ/Pm/sS\n3LUkO6HcPhVusJFTHWwBUJSTm4AmyfwSyBgIPu4gEwwGDYNggcFu0KPgpjXX19c1OjpqCsKtrS2r\nXz2ebQ8FQLV6vW60JNQgSr+xsTEdPXrUatnh4WF97nOf00svvaQ33njDaDxppws4l8tZ+g+gSPYz\nPT0taRsIjcfjFjTI9sB3ABw3NjZUrVYtc4CSJRCVSiWVy2XNzc0pHA6behTx3fT0tOEneGHSeUw2\n4WaJaJBbX1/XwsKC6UKKxaIFUnd3L+85lUrpO9/5jk6fPq2JiQlNTk6qo6NDr7/+unXpIldfXl7W\n1atXrdENERpZLAG2p6dHi4uLpu9Ay8JdJOgsent7jWFD0UlpVqlUrGWdrAylLXgI2Rjfx+dfWloy\nP4xIJKJisfjRZkl2+3B6vV5FIhHV63Xr+EPSjWgFFJ8yJBaLmboNWooNTzaCOQp+hm6JuFuI5S5N\n8NsA/5B2qFjUe/39/bbJHMdpoVzdbEhXV5fVkW5mh6AIJkLAgNsfHh62zYxYh6Dn9XptAQMMwigR\nBNva2pTJZKzHhk0IbTs2NmY6CprQmDO9D43G9g1nhULBGA4kyvTbcDE0zlcwU4BuZE3cbM672Nra\n0vj4uAVTt3qyv7/fQGHAXKTQMAd9fX2qVquWDaG4LBaLisfjWl5etg5OSi96LKLRqK2zra3t6wDG\nxsb0ve99T4uLi/L5fPrZn/1ZDQwMqFqtWkdoPB5XX1+fBgYG5PF4DFegs5WskU2fSCRMx8GlRwS+\narWq3t5es0UksIKVZLNZ06WQZYB1UWo7jmPtAPF43HpvWBO9vb0aHR1VuVxWqVRqES7uZeyrgCHJ\naDc66vz+7ct0aLQJBAKSdsQvUHK79QvLy8vq7+9XIBAwRJsNieGrG5dwU554gbrtyzi1WKBsQOaK\nuSrGwWx6t4YCEIpF4u6qlWSlCV2cbgOceDxupzLz4bOim+DC3v7+fgu+BBT6Go4dO2amLO6W9Vwu\np3K5bM+GPwEH3c8FUBp9BL06bvq0t7fXsg9JdsM6mApSbZoJAQEJ3oi1COoAt16v17Ixgl4sFjOD\nX9i1YrFoASAajbaUhmhM2traVCgUWvo5wBza2tp07NgxvfLKK/r+97+vTCajrq4ufepTn9Lzzz9v\nIqxEImFlLOxQo9HQ8PCwisWistmsBfuVlRVr2Ufi3dHRYf0/zA3an4Y9hGmHDh0yipab7LAHQFXb\nbDbtMIGiJpPFFDkQCJgBM+9jr2NfBQxONLIDaDq4fAAhFIfUos3m9g3m0vYpg7EvpwtR2G2Ig+fj\n9XhoTgN3OzB1M6UIX3djDbw8+k4IRKTUw8PDWl9ft14A5oscnlIHABEdRVtbm90pgnIzEAgYLTgw\nMNBCpVGX9vb2GpBWrVYtrV1YWFAoFLLMh6a+SCRiAOfy8rIxMHfccYepD92oPYIpngUpMWn40aNH\ntb6+rnw+b1kUEnSyEUpGv9+vkZERzc/PS9rOoMLhsKXvlBBke7zfxcVFK+HW1tYsZQf0y+fzSqVS\n1mTW0bF9+xuS7WQyqcnJSet2lmTBpVwu6yd/8if1wgsvqFar6dy5czpy5IgGBwf1xhtvyHEcnT9/\nXvF4XNVqVUNDQ3YHarlc1tmzZ82jc2VlxUyJJiYmzL+Td09DY7PZbJGIg1uAcWxubtphg0iLXhuC\nP3MnUHHtArgQmFM6nbbnvdexrwIGTTOo7zY2NhSLxZTJZFQulzUzM6OhoSFrl8ZzAbaAE3Zra0sT\nExNaXl5WPp/X+vq6+UFUKhUlk0nDOSQZQ0JGgGCGGhiMAZkvabokSz1xMqrX61pbW7MFSXpKtiDJ\n1InUn6TtBA1JZluPkQw9IaTV1WrV0HqQ+fn5eXV1dZnuwN2zAWj38ssvq9ls6pFHHlEgENDbb79t\nLltdXV2GU8RiMWMpkFRLO+K6a9eu2eVFsVjMSjC6Z2dnZy1Q4yhODwNsTCAQsEY1BHr8P7IpMAm/\n32/NVFyNEAwGNTo6airUbDZr7f/Ly8uWml+9etWYATaW3++30pUWb7/fbxQ1nwuzm3w+rxMnTui+\n++7T0aNHdeXKFc3Ozhr122g0dObMGdNTDA4OKp1OG86wsrJitnrT09OGK8zPz7fcmjY+Pm54BIdH\ntVo1lyy3XojyJpfLKZ1OW8YGvtTe3q65uTn72e5SmfWQSqVuaI/uq4BBuYHhLuDn3NycgsGgMpmM\nLbj+/n41Gtt3RlYqFVNx0nwlbdf+LOZsNmsdjvl83hadO8V305iUOwCrBA7+W9pxBUcUNDk5aRsP\nutRN2xJowCTcSk16C/A7WF9fN4ARifji4qLi8bjOnDnzHok22RNSYZqZvF6vksmkicqOHj1qd6j6\nfD595zvfUXt7u+6++269++67Jhyj4YzsJBKJmOoymUya2hOmh2sKoFOHh4ct5Ud45Wae0um06WLy\n+bzdYUIZgsYFV/J0Om1laSaTUTabtWcPy3D48GHNz8+b0Q7BGKaNUrVarapWq6lQKCibzSqTyZiX\nBl3P8/PzRsMnEgktLCzo6tWrunDhgh544AElEgktLi5aIyLlKxgWStT+/n7DCtB7LC4uWotCKBQy\nIeHm5qamp6dtzuhzsFVgHfD5KOEGBgaMZnY7l7n7nWCpGo2GRkZGNDU1ZdL3Gxk3ZrdzkwcPJBQK\ntVCh1P5sWHALFHswIgBJSL7h1SWZQEbaDg4TExNKJBItkm1AI9JBt+EK6j33picb8Hq9JnAiXU8k\nElaSkF3AvhAMwRnc2QZpKXQoZRjZAjhBPB43tgi6EacsMieYAhZfe3u7UqlUi1tYKBRSZ2enTp48\naY5iy8vLunz5smFGqCqhJZn71taWBV9Uh1CQNKc1Gg0lEgnTDECNk6mB2bDhhoaGzDlLks13ZWVF\nkUjEunABPXkXfFZ3t+309LQuXbqkQqGgc+fOaXp62up7WIp4PG5mQNVqtQVHgnJlTZZKJb3xxhta\nW1uzaxjch1R7e7uJ1di4lJdkUn19fXZjXTKZNG0L75hgz/tmDdDMCL6BuhOtD6C0m2GDLTt06JBZ\nAEAH9/b2KhwOK5/P39Ae3VcZhluZt7q6qswPXbcQvVQqFcs6OFlxlgL0hF1BLk1KS+nQbDZNbkv6\nBgDKiYUEHUEUKkDqTvd8yVBAtqn73fgF0Z7/pp7n6zRoAY66VaQg5Bj/5HI5K4H4/MFgUOFw2Brz\nsJRDDYhOgHIlHA6rra1N8/PzlpEgbkokElY+wCy56U28MaPRqNrb25XNZk2FSVCF/ZBkFy2hryCY\ng0kxJ2ToAKCwVWQcZH7gDDw397sgkLhpbk5uNgnvAy0L75ASBVqTcon2eKwCL1y4oMnJSd1yyy0a\nHBw0VS6nN4fAwMCAyuWydctyJSelAboasp2JiQmT0AOww7zBBrIm3EbC7s/qOI49KzJY+n0QzrW1\n7dxnsrq6+tH2w2ARQKfCZRcKBUsNuWWLkwaNfVdXl1FJxWLRdACgzqTK6XTaFjAgEAOKlBIDkxzq\najr+pB3ZOC+HBbO4uKhsNmsAHYFjd5MZQYhanZ9FUCGbQv24urqqbDarVCqlRqNh6TOBDm8GNgYn\nLh25fr/fQE02w9zcnBqNhg4fPqxQKGS6DhY2tCtelNKOxoRNSB0N8Mu7YROSAbrVo3T5Il1n06Hw\nlbaVjdCCHA702xB4yM7ITMF1ABqRiLe3t2toaEhTP7ydzOv1WlMZBwPPEM8NepAAjnt7e1UoFFQs\nFvXKK6/o1ltv1Z133qkXX3zRKFYYBxrnyO4ajYaZPQEME7SRdrOupJ2b6/AEQflJEOHwoiU+nU4b\nDgIm48YzAMxZS7VazWhc/Fr3OvZVwKhWq1aXdXd368SJE9aqzgMjba5UKsZbU5d6PB7F43FD+re2\ntqyBCHUdTUqApmQmklq0Exi5+P1+dXZ2WmoIeMWJ5vF4zI2ZdM9xHKMa3R21pMJuKz93aUPQIOWU\nZG5XbW1tpmBsb2+3hjv0IgjR2CAwSbAafr9fyWTSmBZpm9psNBp2Z0W9Xtfi4qJpQyRpfHzcrgNc\nXV21hir0CLTxRyIRS7u7u7utGxXwutFo2GXHOGIj5GIT5/N5w1bcdDGX9aysrOjUqVOmgkRa3mg0\nlE6nTQMBuwQO0d3drYWFBZVKJQ0NDRmuQ9AmALe1tWlwcNCo2MXFRSudwuGwstmsLl26pJMnT2p2\ndtaukqSkIPiAveDQzd044AyUEPh0coBRvtEztbS0ZDR9sVhUIpFQMplUqVQy7AsWzOfbvjOGUoi/\nh6YJ7A/VbHt7u6LRqJV+ex37KmDQcYcik5c3MzNjl81EIpGWrj/KEzQSiFkwyOH6AbINegiazaal\n2Sx0NxvCJiN7AZ/A+o1UsNls6vDhw3YVAguHvgpYBk55AEBOauhatw6EYELGlc/nba7pdFrVatVc\no2BTEDadOnVKPT09dqUfCkb+Lq5kKGRB0nleuy/2JZOZmZkxILharSqVSpk0m8wlHo+rVqsZU0SN\nX6lULADDQOCOPT4+rkKhYPQnhwKKX0olMJdbbrnFBFOk8ygW2bgzMzNWVvAuLly4oLa2Nl27ds2w\nnvX1dc3NzWlsbMzs7Wq1mikvoWLJIG+//XblcjmVSiW9/PLL+s3f/E0dPnzY2Aeud+QeHfxTofSh\ngx3HsU7VWCxmgDE0eblcVrlcViAQ0ODgoC5dumSy/EuXLlnpAoifzWblOI4FLHAy3MuTyaQZC/H+\nySDJ6PY69lXASCaTBhjhfQCiDD4wNTUlj8djfRXo+TltkB7zEkCZr169qmAwKJ/Pp8uXL2tkZKSl\ne5TygM2NQo6TjHSVVFzacfF2u2DT7ckm8vl8SiQSpizc2toysRNZC4IyN6gq7TS6ra2tWeMWqTwi\nHOrycrms7373u3aqra6uamZmxuwCqIdJVZFqnzt3TolEwuYKqg/GwJ+xWMzYqEajocnJSaMswSnY\nXJQF9EEAaqZSKS0sLJgJMJuKZ9hoNFQoFEy+7GZXyAoXFhYUjUY1Nzdn2QlUr8/n0+zsrGEi2AB4\nvdt3cLhLRPCjeDxuremBQMAwi3A4rHPnzrUA0R0dHTaHEydOqFQqWQbGxUaDg4Mt4DGK2bm5uRYz\nJLdMHuVrPp9XX1+fbrvtNgOZz5w5o42NDaXTaQt44E6sp46ODsXjcRUKBaVSKesdqVar1nXL5c9I\n0MHEKFP2OvZVwMD5iHQSp2c8L1KplLLZrOEK6B0ApeC8YVi8Xq9yuZzpNfD3ZFFks1nr6qQUktRS\na6IBQO/hNjjBvbqrq0vpdNpeEhRWX1+fcrmcBgcH7fe4VaUIcfh97g2KOMzn8ymbzVq5xvf19fWp\ns7NT2Wy2RcOxurqqubk5M5fhdKa0o1ThcmL6MJBR03xG+za1PqcVTEIsFjNxV3d3t10KjHJ1bW1N\ni4uLSiaTRhnncjlLvaVtPQqZHWBzf3+/uru7dejQIUujYUlo4spms1paWtLQ0JBdBlSpVJTP5zU2\nNmYeIZQTs7OzhkPgExKJRMwDNBaLGVAOvkB/EhgNbekwZ4uLi3r11Vf16U9/2t4VGRsaC+h1mJNM\nJmOl8ZEjR5TP5y1b44a56elpAzCnp6cVjUYty+SZkenSrEaZeu3aNWNlUOpyCBaLRet1oaTGuetG\nxp4CxhNPPNFyacyf/dmfaWlpSV/72teUz+cVj8f1+c9/3oCxb37zmzp58qQ6Ojr0xBNPGNvxo8bl\ny5ft4TvOduPYwMCALerFxUVT0rmvTOQEJgUslUrGbyN/lmR9Dpubm5aqS603sEsyUInSBizD3QdB\nuXHkyBF1dnbqypUrJjhjoRIA4cLdNoCcWgQjN3jJZ4GlYGEPDw9rYWHB+Hw3vdnT02M0Macyv9vj\n8Vit7ZZm03exubmpVCplAN3Gxoa1TKdSKZVKJcViMc3OzhplCiJfq9Wsv8TNVFEjo+ZEOUuWMzo6\nap+BAAQGBGMEHsPp7vP5FIvFNDc3p4GBAQMlKROhlOntoRcFinRxcdEysFKpZKc0NgkA11ziBCME\njtXR0WE06Nramt5880393M/9nDmhA16SkZJ1dnd3m1sXpSv4Sb1e15UrV6y7NxKJGKPEwQiQjyMX\nhwHPnqDH51hZWVGxWFQsFrM+HjLZlZUVO2Rhum5k7ClgeDwePfXUU4YHSNILL7yg22+/XZ/+9Kf1\nwgsv6Pnnn9ev/dqv6cSJE1pcXNTXv/51Xbp0Sd/4xjf0pS99aU+TYbHBKqC7p02au0rg7JPJpHHL\nPGyoR6IrG5OTfDf/v7u1XZIxIqR7W1s7d1i6n4njOBoaGjJQEgzErXuggxJMg5LDLQhjDvSWkKHM\nzMxYZ6rf71c4HNb09LQBa1DMnIp08lLLX7161cRSGNZAC+NsxWmDwnZ2dtZMjMAsBgcHjdqFugU7\nIXCw+Sh9wDoI7nQaYxxcLpeNNsbeLxQKWY8J+MfKyoqZ5PT19dn3UvJUKhWVy2WlUik7aQm07t4J\n1JeUlGRdzWZTMzMz8nq9KhQKJqpjPtCilLDZbFYbGxs6ffq0FhYW9MYbb+jhhx/W2NiYZmdnFY1G\n37OWAal9vm1bSDAFAjkNcQgS+ft4w7hb9oeHh63jenBw0NYGOBTrAfCzra3NbqNbWFiwLBXX8RsF\nPfck3HLX1Yy33npL999/vyTpgQce0FtvvSVJevPNN+3rH/vYx6wVeC9jYGCgxUJfkmkyqtWqpqen\nTeXYaDSMOnX7GXZ0dOiOO+6Qx+Ox2664EBgwSdpWlSLicTcm0V/hZjPILChzpO0sBDqLTYOYR5Ix\nO47jmO8GdTOZBBkSQJRblp5MJg2c9Xg8djmRJNtQlFrui5u4BIf0n9+ZTCYNEEZKT+CF6QDzIQAg\n8KIFG49KgiG9GdB+tFDn83mTNRM0MS4GAAS45k+sAHnu09PTLXPM5XKmzCwWi7p06ZK2trbvaqWM\nQYQ1NjamQ4cOGZU8ODiohYUFzc7OGkCK0IoyjSwuFAopkUjYJiQrwoqwVCopHo8bQPraa6/JcRyN\njY1Zh+7c3JyJD1dXV1UulzU9PW1MD0Gpr6/PenKgfAnUHCjgE8lkUhMTE4bDUFq6HcH5TBsbGxob\nG7MMgrV3yy232IEERsh+2OvYc4bxpS99SR6PRz/zMz+jhx56yNJZSZa6STITF0Y4HLYS4UcNbvBi\nY1LTx+NxMxvBMYkAhoITLp7mHWgzfBYB87a2tt2xc7mc6vW6bVJo2N0BAz0B1Jcky1rcwCnpHt+H\niQ4Br1Qq2eW6lCuk2yg9ySbcmgYkxdCTSHndrl7Ly8vK5XLW/k52BN+OYS/qwXq9bt6QwWBQ/f39\nNn+cva5evaqVlRUNDg7qxIkTSiQSpp71+XxGfxK8t7a2VCgULF3OXWz+AAAOS0lEQVSORqMaHBw0\nEBvZMgK6tbU1C1LQfyhcNzc3zS0cYA4NDIcD2SLBCyCQ34EXbL1eVywWM9yHdnbSd/Qrt912m11Z\nsbi4aEAwbfLcNidt93FkMhmdP3/ebPsmJiZ08uRJ02+cPn3aqE1ASIR2MCAwe7FYTIcOHTImjOY4\nBHOYPmEShPjQ7W5GRy53rwaDQZ09e9bKJ5/Pp/HxcduTtCDclG7VL37xi3bZ7Be/+MUb1p+7xVH/\n12BBHj58WGfOnFEwGNT8/LzZwQGC4ZeQSCTsYV6+fFnnzp1TJpOxG6Hm5+etPwONAqmspBYQkhOe\nU5Ba3q3IZINvbGwoHo8rk8kolUrprbfesr4JTFkIPpLM3QvqjoytXC6rWCyawAr8RtrJ6hxn298g\nHo9rfX1d2WzW8CRqZrAHAnckEtG1a9esPHF3uJLGgw+BO3V0dGhhYcEAS/pFZmdnNTo6qtnZWRMA\nsflZ4AjR0FOQ/lLmDQ8PG3sEWn/p0iVTMWazWTvVuQtUkv3urq4uzc7OynEcwxaazaZRy7wbRGGU\nMAQyAoBbvUq5KW0bN9Grwk3nxWLR5NSpVEp9fX3WwNje3q5EIqFLly6pWCxqYWFBhw8f1i233KKz\nZ8+abiOZTJp4C+FZs9m0/hqvd/uu2itXrmhxcdEcxGmeQ7wI3gCLQuZHJ/alS5fMaY13jBkVZTdX\nUqJhAke5KdJwsoNAIKDjx4/r8uXLdgs3f1Ibh8NhAx8lmenr7nHmzJmWi3Uef/xx/cIv/IJdhDsx\nMdFii09ZAFjEIqcmPHz4sAFM0GS33367pB2PSOTGtP3SqedWxbndn2BC6Gfhd/t8Pk1MTCiVSqmt\nrU2pVMqo0fX1dTsB6TmQdjpxoR05naUd3YUkO7FJXWnUOnLkiFnDUcagEAWwRRZOC/WRI0fk9Xp1\n7Ngxra+vK51Om7z6rrvuMv8F0tJ4PG50NN2uzIff5eb5KZdoFgM4JEBz6mOjT8nj9Xp19913m5EM\n7wdwcnh4WIODg0bLNps71zO6gyq+GJ2dnabtoAwEPHUD1pyqYEfItNHIeDwe3XvvvdYigEu34zhW\nArp9R2+99VZjUjY3N3XvvfdaoEIECNgNHgFYT7no8/k0NDRkzuJu9qLRaNjnAeMoFovy+XzWGt9s\nNvXggw+abQM+GOwV+o/wHI3FYnrggQesZYLD81/+5V9sDR45ckRHjhy5biz4kQGD+ohU99SpU3rs\nscfMXOTRRx/VK6+8orvvvluSdPfdd+vb3/627rvvPl28eNHs8HeP603q3//93y0FGx8f19TUlMLh\nsMld2TDu9m2Ua6lUyh7owsKCRkdH1Ww27XZvggQZxOuvv65//dd/NW8GmIqlpSUTHpGSoljkdOIh\nnz59+j1y8ra2No2NjalWq2lmZkb/9m//ZkGvu7tb4XDY2Ao2AC9akpnyvPvuu9ZUd+edd+q+++6T\ntE2rkWVw1wb3mR4+fFjXrl2Tz+fT6dOnNTk5qc7OTh09elTlclmZTEbNZlOTk5P653/+Z4VCIWMy\nyGTQC7gPAqhPPt/KyopZ8q+vr2tiYsLKGQIl38e66erq0tWrV42RWl9ft2DPphoeHpbHs234c+LE\nCZ07d07NZlOjo6MtP9vr9SoajWp+ft5EbQStdDpt5U4kElFnZ6dqtZpZBCDbh24eHBzU+fPnlUwm\nrReEND4YDJpQi96jpaUly1Q7Ozv1/PPPGyPz+OOPq1wu66WXXlI0GtWFCxckyWhbspqVlRXdeuut\nchxHuVzOtCXgZTSNoZdxnO0my4mJCZ09e9Yax+jUvnbtmmX9KD/JrFZWVvSJT3zCDHO8Xq/+6Z/+\nyXp4QqGQHnvsMT3++OM/KhTsLWBUq1V9+ctfNrDup37qp3TnnXdqbGxMTz/9tF5++WVFo1H93u/9\nniTprrvu0okTJ/Tkk0+qs7NTv/3bv72nifBh6XFABLO0tGR/4nHApoM2pC7mlD927Jg6OzutEQ3h\nD8BpOBzW/Py8AUhEdncmg24CBoaTyd2yjrdlV1eXCoWCpfcIx/i50o4jOuk2naY8VwYBTdpx1cLo\nttFo6Pz58wqHw6Z4hDaVtn008bEA53E7eUPBIo8HEAOnmZ+ft/lgsgKOgLO429UJFSFBwH1LGu8C\nUZ2blcDUhdZ03Kbm5+ft9xWLRd1///1WWsE0kcGA22CY627qqtfrdmfNG2+8Yf1DmD9TyiErp/cC\nQLNQKKher9v7wbqQTmnUsD09Pdb2f/LkSd133306fPiwvvvd75qXSXt7u3mDRCIRawQ7e/asAdBk\nrzBb4XDYmsIog1hbXAMJ2+f3+5VIJKwnZ3x83DK8kZERY2uQCni9XmUyGaOJ3WtvL+NHBox4PK4v\nf/nL7/l6b2+v/uRP/uS6f+czn/nMDU3CJvNDSTBKO+hNmACk3mQZnGTQbY7jKJlMGogH8EatVywW\nlclkrCPRjWGw6dxMCYAmrAYbkE3lptFGR0dVr9cVCoUUCARaLgbeTdfS44GvJymwu6OVBdHX16d0\nOm19DZxWpVLJLhA6fPiw5ubmbMPk83mjLHFywu7N7fwEZlMul63blg1LDwpqRhYz5SWZFh2ZKFd7\nenpMzsypyUXOiJnYnF1dXS3t1gQqtC+Ixti87e3tdive5uam+bdms1klk0llMhkDDNvb2023g0CM\nPo6NjQ2r+cEDMCHK5XJ20k9OTqq/v98yV4/HY9cq4HI1NjYmx3E0OTmpU6dO6eGHH9bRo0dVKBR0\n9OhR8/K4du2atdCPjY2ZHaIbV+nt7TV5PBcVsT7JlqGJAZIdx1EsFjNn9M7OTqO9AXaj0ahmZmYM\nEKY5EXbwRsa+8sNgw7O4E4mEIc10jhI0YB+QdHu9XgMjaVvmBcD/IymnfpN2nLzdD85Nn0o7GAO1\nMd8Lq4FOg5SvUCgYwOX+Pfwsfo7boFWS/bs7iBHAYFIAbWm2Y17UqaSkZE9uPxBwBRzG0XKsrKzY\n5kGCDZNB8MEmEQ0DfD+ZBhkFdCzKz+XlZfPLQMWLjJw6G+yIlmxKQ5q4yEDoeOW5h0Kh92hGeI7u\nFgPWDOUOmRHzRrMAswYNvLy8bGVwoVBoEfth4BuNRo1VA+9AQJfJZAy38Pv9Zpzs9pklE43H43Yp\nNX0uvHOYQVgcslaa9PA1HR4etoZKKFwkBW7NDkpfDqobGR5nt8DiYByMg3Ew3mfsmwzDjdJ+FMZH\nab4fpblKB/O9meP/O9d9EzAOxsE4GPt/HASMg3EwDsaex74JGO8nFNmv46M034/SXKWD+d7M8f+d\n6wHoeTAOxsHY89g3GcbBOBgHY/+Pg4BxMA7Gwdjz2BcWfSdPntS3vvUtOY6jBx98UI8++uiHPSU9\n99xzevvttxUMBvWVr3xFkm6Ky9iPYxSLRT377LOmQHzooYf0yCOP7Mv5bm5u6qmnnrKmtnvuuUe/\n/Mu/rFwup2eeeUZLS0s6dOiQnnzySWvQe/bZZ3XlyhX19fXp85//vKlrP8jRbDb1x3/8xwqHw/rD\nP/zDfT3fm+qQ53zIo9FoOL/zO7/j5HI5Z3Nz0/mDP/gDZ3Z29sOelnPu3Dnn6tWrzu///u/b1/7h\nH/7BeeGFFxzHcZznn3/e+cd//EfHcRzn7bffdv70T//UcRzHuXjxovOFL3zhA51ruVx2rl696jiO\n46yurjqf/exnndnZ2X0737W1Ncdxtt/9F77wBefixYvOV7/6Vee///u/HcdxnL/5m79x/uu//stx\nHMf59re/7XzjG99wHMdxfvCDHzhPP/30BzpXxosvvug888wzzp//+Z87juPs6/k+8cQTTr1eb/na\nj2stfOglyeXLlzUwMGANRJ/85Cf15ptvftjT0i233GL3dzBuhsvYj2OEQiE7FTo7O5VKpVQsFvft\nfPGhQIbt8Xh05swZ/cRP/IQk6f7777c14J7rPffco9OnT39g82QUi0WdOHFCDz30kH3t3Xff3bfz\ndW6iQ96HXpJcz6ELK7r9Nm6Gy9iPe+RyOU1PT2tiYmLfzrfZbOqP/uiPtLi4qIcffliJRMLcySQp\nEomoVCq9Z65er9d6Sdz+sjd7/P3f/71+/dd/3RzXaF7cr/O9mQ55H3rAuN7Yq0PXfh4fxmdYW1vT\nV7/6Vf3Gb/zGDZu7fpDz9Xq9+ou/+AutrKzoK1/5iubm5vY8n90n580e4FiZTMYMn653gu+X+Uo3\n1yHvQw8Y4XC4pQuwVCrdsM/gBzX+vy5jN3M0Gg395V/+pX76p39ax48f3/fzlWTGyRcvXrTOWa/X\n2zIf5sqt8bh6fVDj/Pnzeuutt3TixAm79+Zb3/qWdYDut/lKN8chj/GhYxjj4+PKZrN2f+cPfvAD\nc+/6sMfukwSXMUnvcRl79dVXJen/dBm7meO5557T0NCQHnnkkX0931qtZqk9dv1DQ0M6cuSIXn/9\ndUnSq6++et25vvbaazp69OgHMk/Gr/7qr+q5557Ts88+q8997nM6evSoPvvZz+7b+e42aTp16pSG\nh4d/bGthXyg9T548qb/7u7+T4zj61Kc+tS9o1WeeeUZnz55VvV5XMBjU448/ruPHj+vpp59WoVAw\nlzGA0b/927/VyZMnzWVsdHT0A5vr+fPn9dRTT5nFncfj0a/8yq9ofHx838332rVr+uu//mvzBbnv\nvvv0S7/0S8rlcvra176m5eVlZTIZPfnkk2ZU+1d/9VeamppSX1+ffvd3f1fxePwDmevucfbsWb34\n4otGq+7H+eZyufc45D366KNaWlr6sayFfREwDsbBOBgfjfGhlyQH42AcjI/OOAgYB+NgHIw9j4OA\ncTAOxsHY8zgIGAfjYByMPY+DgHEwDsbB2PM4CBgH42AcjD2Pg4BxMA7GwdjzOAgYB+NgHIw9j/8F\nu/LyzLjanqoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dbc40f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's first load an image.  We're going to need a grayscale image to begin with.  skimage has some images we can play with.  If you do not have the skimage module, you can load your own image, or get skimage by pip installing \"scikit-image\".\n",
    "from skimage import data\n",
    "img = data.camera().astype(np.float32)\n",
    "plt.imshow(img, cmap='gray')\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice our img shape is 2-dimensional.  For image convolution in Tensorflow, we need our images to be 4 dimensional.  Remember that when we load many iamges and combine them in a single numpy array, the resulting shape has the number of images first.\n",
    "\n",
    "N x H x W x C\n",
    "\n",
    "In order to perform 2d convolution with tensorflow, we'll need the same dimensions for our image.  With just 1 grayscale image, this means the shape will be:\n",
    "\n",
    "1 x H x W x 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 512, 512, 1)\n",
      "Tensor(\"Reshape_3:0\", shape=(1, 512, 512, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# We could use the numpy reshape function to reshape our numpy array\n",
    "img_4d = img.reshape([1, img.shape[0], img.shape[1], 1])\n",
    "print(img_4d.shape)\n",
    "\n",
    "# but since we'll be using tensorflow, we can use the tensorflow reshape function:\n",
    "img_4d = tf.reshape(img, [1, img.shape[0], img.shape[1], 1])\n",
    "print(img_4d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of getting a numpy array back, we get a tensorflow tensor.  This means we can't access the `shape` parameter like we did with the numpy array.  But instead, we can use `get_shape()`, and `get_shape().as_list()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 512, 512, 1)\n",
      "[1, 512, 512, 1]\n"
     ]
    }
   ],
   "source": [
    "print(img_4d.get_shape())\n",
    "print(img_4d.get_shape().as_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The H x W image is now part of a 4 dimensional array, where the other dimensions of N and C are 1.  So there is only 1 image and only 1 channel.\n",
    "\n",
    "We'll also have to reshape our Gaussian Kernel to be 4-dimensional as well.  The dimensions for kernels are slightly different!  Remember that the image is:\n",
    "\n",
    "Number of Images x Image Height x Image Width x Number of Channels\n",
    "\n",
    "we have:\n",
    "\n",
    "Kernel Height x Kernel Width x Number of Input Channels x Number of Output Channels\n",
    "\n",
    "Our Kernel already has a height and width of `ksize` so we'll stick with that for now.  The number of input channels should match the number of channels on the image we want to convolve.  And for now, we just keep the same number of output channels as the input channels, but we'll later see how this comes into play."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 100, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "# Reshape the 2d kernel to tensorflow's required 4d format: H x W x I x O\n",
    "z_4d = tf.reshape(z_2d, [ksize, ksize, 1, 1])\n",
    "print(z_4d.get_shape().as_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"convolvefilter-an-image-using-a-gaussian-kernel\"></a>\n",
    "## Convolve/Filter an image using a Gaussian Kernel\n",
    "\n",
    "We can now use our previous Gaussian Kernel to convolve our image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 512, 512, 1)\n"
     ]
    }
   ],
   "source": [
    "convolved = tf.nn.conv2d(img_4d, z_4d, strides=[1, 1, 1, 1], padding='SAME')\n",
    "res = convolved.eval()\n",
    "print(res.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two new parameters here: `strides`, and `padding`.  Strides says how to move our kernel across the image.  Basically, we'll only ever use it for one of two sets of parameters:\n",
    "\n",
    "[1, 1, 1, 1], which means, we are going to convolve every single image, every pixel, and every color channel by whatever the kernel is.\n",
    "\n",
    "and the second option:\n",
    "\n",
    "[1, 2, 2, 1], which means, we are going to convolve every single image, but every other pixel, in every single color channel.\n",
    "\n",
    "Padding says what to do at the borders.  If we say \"SAME\", that means we want the same dimensions going in as we do going out.  In order to do this, zeros must be padded around the image.  If we say \"VALID\", that means no padding is used, and the image dimensions will actually change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11df01278>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a3PGQ/jOz4HUWGORsNBouq0Dmoa6ILtqyplL5QzcMGCg84nM6ZBEKHFpPrD/e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\nkmUIGEMZylCSZQgYQxnKUJLl/wPPp2oZoXX+PwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11df017f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib cannot handle plotting 4D images!  We'll have to convert this back to the original shape.  There are a few ways we could do this.  We could plot by \"squeezing\" the singleton dimensions.\n",
    "plt.imshow(np.squeeze(res), cmap='gray')\n",
    "\n",
    "# Or we could specify the exact dimensions we want to visualize:\n",
    "plt.imshow(res[0, :, :, 0], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"modulating-the-gaussian-with-a-sine-wave-to-create-gabor-kernel\"></a>\n",
    "## Modulating the Gaussian with a Sine Wave to create Gabor Kernel\n",
    "\n",
    "We've now seen how to use tensorflow to create a set of operations which create a 2-dimensional Gaussian kernel, and how to use that kernel to filter or convolve another image.  Let's create another interesting convolution kernel called a Gabor.  This is a lot like the Gaussian kernel, except we use a sine wave to modulate that.\n",
    "\n",
    "<graphic: draw 1d gaussian wave, 1d sine, show modulation as multiplication and resulting gabor.>\n",
    "\n",
    "We first use linspace to get a set of values the same range as our gaussian, which should be from -3 standard deviations to +3 standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xs = tf.linspace(-3.0, 3.0, ksize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then calculate the sine of these values, which should give us a nice wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116306208>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pwjmQ6KaQFm1MRyEKaCyH85CBw4s/4vhmu+koAUF3fwPdvgky+G+moxAFPJbD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\nJuaA/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ff17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ys = tf.sin(xs)\n",
    "plt.figure()\n",
    "plt.plot(ys.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And for multiplication, we'll need to convert this 1-dimensional vector to a matrix: N x 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ys = tf.reshape(ys, [ksize, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then repeat this wave across the matrix by using a multiplication of ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11636d2b0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EIkGhUAgeBHPDwFOeJj/484LMli04CL7++mtOnDjBkSNHqK+v5+DBgwwPD99yu9vtP3K5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f614630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ones = tf.ones((1, ksize))\n",
    "wave = tf.matmul(ys, ones)\n",
    "plt.imshow(wave.eval(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can directly multiply our old Gaussian kernel by this wave and get a gabor kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11645f6a0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ScwTiymgRgVWntCrjfM1PLaSX2pX6YfVTSmslOkII8Cqs2QDI0QNXBxclUDst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\ngP8HQW9HoVJvpMAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d669cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gabor = tf.multiply(wave, z_2d)\n",
    "plt.imshow(gabor.eval(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"manipulating-an-image-with-this-gabor\"></a>\n",
    "## Manipulating an image with this Gabor\n",
    "\n",
    "We've already gone through the work of convolving an image.  The only thing that has changed is the kernel that we want to convolve with.  We could have made life easier by specifying in our graph which elements we wanted to be specified later.  Tensorflow calls these \"placeholders\", meaning, we're not sure what these are yet, but we know they'll fit in the graph like so, generally the input and output of the network.\n",
    "\n",
    "Let's rewrite our convolution operation using a placeholder for the image and the kernel and then see how the same operation could have been done.  We're going to set the image dimensions to `None` x `None`.  This is something special for placeholders which tells tensorflow \"let this dimension be any possible value\".  1, 5, 100, 1000, it doesn't matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, ?, 1)\n",
      "[1, None, None, 1]\n"
     ]
    }
   ],
   "source": [
    "# This is a placeholder which will become part of the tensorflow graph, but\n",
    "# which we have to later explicitly define whenever we run/evaluate the graph.\n",
    "# Pretty much everything you do in tensorflow can have a name.  If we don't\n",
    "# specify the name, tensorflow will give a default one, like \"Placeholder_0\".\n",
    "# Let's use a more useful name to help us understand what's happening.\n",
    "img = tf.placeholder(tf.float32, shape=[None, None], name='img')\n",
    "\n",
    "\n",
    "# We'll reshape the 2d image to a 3-d tensor just like before:\n",
    "# Except now we'll make use of another tensorflow function, expand dims, which adds a singleton dimension at the axis we specify.\n",
    "# We use it to reshape our H x W image to include a channel dimension of 1\n",
    "# our new dimensions will end up being: H x W x 1\n",
    "img_3d = tf.expand_dims(img, 2)\n",
    "dims = img_3d.get_shape()\n",
    "print(dims)\n",
    "\n",
    "# And again to get: 1 x H x W x 1\n",
    "img_4d = tf.expand_dims(img_3d, 0)\n",
    "print(img_4d.get_shape().as_list())\n",
    "\n",
    "# Let's create another set of placeholders for our Gabor's parameters:\n",
    "mean = tf.placeholder(tf.float32, name='mean')\n",
    "sigma = tf.placeholder(tf.float32, name='sigma')\n",
    "ksize = tf.placeholder(tf.int32, name='ksize')\n",
    "\n",
    "# Then finally redo the entire set of operations we've done to convolve our\n",
    "# image, except with our placeholders\n",
    "x = tf.linspace(-3.0, 3.0, ksize)\n",
    "z = (tf.exp(tf.negative(tf.pow(x - mean, 2.0) /\n",
    "                   (2.0 * tf.pow(sigma, 2.0)))) *\n",
    "      (1.0 / (sigma * tf.sqrt(2.0 * 3.1415))))\n",
    "z_2d = tf.matmul(\n",
    "  tf.reshape(z, tf.stack([ksize, 1])),\n",
    "  tf.reshape(z, tf.stack([1, ksize])))\n",
    "ys = tf.sin(x)\n",
    "ys = tf.reshape(ys, tf.stack([ksize, 1]))\n",
    "ones = tf.ones(tf.stack([1, ksize]))\n",
    "wave = tf.matmul(ys, ones)\n",
    "gabor = tf.multiply(wave, z_2d)\n",
    "gabor_4d = tf.reshape(gabor, tf.stack([ksize, ksize, 1, 1]))\n",
    "\n",
    "# And finally, convolve the two:\n",
    "convolved = tf.nn.conv2d(img_4d, gabor_4d, strides=[1, 1, 1, 1], padding='SAME', name='convolved')\n",
    "convolved_img = convolved[0, :, :, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we've done is create an entire graph from our placeholders which is capable of convolving an image with a gabor kernel.  In order to compute it, we have to specify all of the placeholders required for its computation.\n",
    "\n",
    "If we try to evaluate it without specifying placeholders beforehand, we will get an error `InvalidArgumentError: You must feed a value for placeholder tensor 'img' with dtype float and shape [512,512]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m    696\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m    698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/errors.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m    449\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m    451\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-6bb9dbcbecae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconvolved_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \"\"\"\n\u001b[0;32m--> 555\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[0;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   3496\u001b[0m                        \u001b[0;34m\"the tensor's graph is different from the session's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3497\u001b[0m                        \"graph.\")\n\u001b[0;32m-> 3498\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    370\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 372\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    373\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m       results = self._do_run(handle, target_list, unique_fetches,\n\u001b[0;32m--> 636\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    637\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m       \u001b[0;31m# The movers are no longer used. Delete them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    706\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m--> 708\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m    709\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n"
     ]
    }
   ],
   "source": [
    "convolved_img.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's saying that we didn't specify our placeholder for `img`.  In order to \"feed a value\", we use the `feed_dict` parameter like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidArgumentError",
     "evalue": "You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m    696\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 697\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m    698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/errors.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m    449\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m    451\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-15318c524e54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconvolved_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcamera\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \"\"\"\n\u001b[0;32m--> 555\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[0;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   3496\u001b[0m                        \u001b[0;34m\"the tensor's graph is different from the session's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3497\u001b[0m                        \"graph.\")\n\u001b[0;32m-> 3498\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    370\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 372\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    373\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m       results = self._do_run(handle, target_list, unique_fetches,\n\u001b[0;32m--> 636\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    637\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m       \u001b[0;31m# The movers are no longer used. Delete them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    706\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m--> 708\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m    709\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: You must feed a value for placeholder tensor 'ksize' with dtype int32\n\t [[Node: ksize = Placeholder[dtype=DT_INT32, shape=[], _device=\"/job:localhost/replica:0/task:0/cpu:0\"]()]]\nCaused by op 'ksize', defined at:\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 171, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/runpy.py\", line 86, in _run_code\n    exec(code, run_globals)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/traitlets/config/application.py\", line 596, in launch_instance\n    app.start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelapp.py\", line 442, in start\n    ioloop.IOLoop.instance().start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/ioloop.py\", line 162, in start\n    super(ZMQIOLoop, self).start()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/ioloop.py\", line 883, in start\n    handler_func(fd_obj, events)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/kernelbase.py\", line 391, in execute_request\n    user_expressions, allow_stdin)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/ipkernel.py\", line 199, in do_execute\n    shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2809, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-45-4eb6dd4e6c0d>\", line 24, in <module>\n    ksize = tf.placeholder(tf.int32, name='ksize')\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/array_ops.py\", line 895, in placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/gen_array_ops.py\", line 1238, in _placeholder\n    name=name)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/ops/op_def_library.py\", line 704, in apply_op\n    op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 2260, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/Users/pkmital/.pyenv/versions/3.4.0/Python.framework/Versions/3.4/lib/python3.4/site-packages/tensorflow/python/framework/ops.py\", line 1230, in __init__\n    self._traceback = _extract_stack()\n"
     ]
    }
   ],
   "source": [
    "convolved_img.eval(feed_dict={img: data.camera()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But that's not the only placeholder in our graph!  We also have placeholders for `mean`, `sigma`, and `ksize`.  Once we specify all of them, we'll have our result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1168f7f28>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aUlSeekoZZPIgRVFkVR29qJRUJV4EoqRRrVbl85//vOzYsUOWl5flq1/9quze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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116637128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = convolved_img.eval(feed_dict={\n",
    "    img: data.camera(), mean:0.0, sigma:1.0, ksize:100})\n",
    "plt.imshow(res, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, instead of having to rewrite the entire graph, we can just specify the different placeholders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x116bea5f8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7xnNTrntdG5bMR7ep+Owwvue+Z5wXSQ2upK0CDVvnmUQ4SbqY6CIb0iedOLay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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116908780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = convolved_img.eval(feed_dict={\n",
    "    img: data.camera(),\n",
    "    mean: 0.0,\n",
    "    sigma: 0.5,\n",
    "    ksize: 32\n",
    "  })\n",
    "plt.imshow(res, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"homework\"></a>\n",
    "# Homework\n",
    "\n",
    "For your first assignment, we'll work on creating our own dataset.  You'll need to find at least 100 images and work through the [notebook](session-1.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"next-session\"></a>\n",
    "# Next Session\n",
    "\n",
    "In the next session, we'll create our first Neural Network and see how it can be used to paint an image.\n",
    "\n",
    "<a name=\"reading-material\"></a>\n",
    "# Reading Material\n",
    "\n",
    "Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … Zheng, X. (2015). TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed Systems.\n",
    "https://arxiv.org/abs/1603.04467\n",
    "\n",
    "Yoshua Bengio, Aaron Courville, Pascal Vincent.  Representation Learning: A Review and New Perspectives.  24 Jun 2012.\n",
    "https://arxiv.org/abs/1206.5538\n",
    "\n",
    "J. Schmidhuber. Deep Learning in Neural Networks: An Overview. Neural Networks, 61, p 85-117, 2015.\n",
    "https://arxiv.org/abs/1404.7828\n",
    "\n",
    "LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521, no. 7553 (2015): 436-444.\n",
    "\n",
    "Ian Goodfellow Yoshua Bengio and Aaron Courville.  Deep Learning.  2016.\n",
    "http://www.deeplearningbook.org/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}